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py-why/dowhy | 882 | Revise user guide entry for intrinsic causal influence | null | null | 2023-02-24 21:36:44+00:00 | 2023-03-07 16:13:45+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/quantify_intrinsic_causal_influence.rst | Quantifying Intrinsic Causal Influence
======================================
By quantifying intrinsic causal influence, we answer the question:
How strong is the causal influence of a source node to a target node
that is not inherited from the parents of the source node?
Naturally, descendants will have a z... | Quantifying Intrinsic Causal Influence
======================================
By quantifying intrinsic causal influence, we answer the question:
How strong is the causal influence of a source node to a target node
that is not inherited from the parents of the source node?
Naturally, descendants will have a z... | bloebp | a1dcccbc805cb28aa4840fe9bce8338278632a50 | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | We did not explain "simple linear relationship" before this text. | kailashbuki | 84 |
py-why/dowhy | 882 | Revise user guide entry for intrinsic causal influence | null | null | 2023-02-24 21:36:44+00:00 | 2023-03-07 16:13:45+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/quantify_intrinsic_causal_influence.rst | Quantifying Intrinsic Causal Influence
======================================
By quantifying intrinsic causal influence, we answer the question:
How strong is the causal influence of a source node to a target node
that is not inherited from the parents of the source node?
Naturally, descendants will have a z... | Quantifying Intrinsic Causal Influence
======================================
By quantifying intrinsic causal influence, we answer the question:
How strong is the causal influence of a source node to a target node
that is not inherited from the parents of the source node?
Naturally, descendants will have a z... | bloebp | a1dcccbc805cb28aa4840fe9bce8338278632a50 | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | Nit [for consistency reason]: intrinsic causal influence | kailashbuki | 85 |
py-why/dowhy | 882 | Revise user guide entry for intrinsic causal influence | null | null | 2023-02-24 21:36:44+00:00 | 2023-03-07 16:13:45+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/quantify_intrinsic_causal_influence.rst | Quantifying Intrinsic Causal Influence
======================================
By quantifying intrinsic causal influence, we answer the question:
How strong is the causal influence of a source node to a target node
that is not inherited from the parents of the source node?
Naturally, descendants will have a z... | Quantifying Intrinsic Causal Influence
======================================
By quantifying intrinsic causal influence, we answer the question:
How strong is the causal influence of a source node to a target node
that is not inherited from the parents of the source node?
Naturally, descendants will have a z... | bloebp | a1dcccbc805cb28aa4840fe9bce8338278632a50 | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | In that case, we would also need to introduce something like ``Y = max(0, X) + N``, since the following train will not leave earlier if the train is faster.
I am not sure if we really need to make it that complicated here in the "how to use the method" section, since this is mostly to show how the API works? I only ... | bloebp | 86 |
py-why/dowhy | 882 | Revise user guide entry for intrinsic causal influence | null | null | 2023-02-24 21:36:44+00:00 | 2023-03-07 16:13:45+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/quantify_intrinsic_causal_influence.rst | Quantifying Intrinsic Causal Influence
======================================
By quantifying intrinsic causal influence, we answer the question:
How strong is the causal influence of a source node to a target node
that is not inherited from the parents of the source node?
Naturally, descendants will have a z... | Quantifying Intrinsic Causal Influence
======================================
By quantifying intrinsic causal influence, we answer the question:
How strong is the causal influence of a source node to a target node
that is not inherited from the parents of the source node?
Naturally, descendants will have a z... | bloebp | a1dcccbc805cb28aa4840fe9bce8338278632a50 | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | Removed "simple" here. This is more referring to the ``Y = X + N`` setup, since users might think it is restricted to linear relationships. | bloebp | 87 |
py-why/dowhy | 882 | Revise user guide entry for intrinsic causal influence | null | null | 2023-02-24 21:36:44+00:00 | 2023-03-07 16:13:45+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/quantify_intrinsic_causal_influence.rst | Quantifying Intrinsic Causal Influence
======================================
By quantifying intrinsic causal influence, we answer the question:
How strong is the causal influence of a source node to a target node
that is not inherited from the parents of the source node?
Naturally, descendants will have a z... | Quantifying Intrinsic Causal Influence
======================================
By quantifying intrinsic causal influence, we answer the question:
How strong is the causal influence of a source node to a target node
that is not inherited from the parents of the source node?
Naturally, descendants will have a z... | bloebp | a1dcccbc805cb28aa4840fe9bce8338278632a50 | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | Fixed | bloebp | 88 |
py-why/dowhy | 875 | Revise attributing distributional changes user guide entry | null | null | 2023-02-17 22:20:40+00:00 | 2023-03-07 16:14:00+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/attribute_distributional_changes.rst | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
What mechanism in my system changed between two sets of data?
For example, in a distributed computing system, we want to know why an important system metric changed in a negative w... | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
**What mechanism in my system changed between two sets of data? Or in other words, which node in my data behaves differently?**
Here we want to identify the node or nodes in the gr... | bloebp | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | Is this really better? Note that we already use the word "mechanism" in our methods and language. So I believe it does make sense to refer to it here, no? | petergtz | 89 |
py-why/dowhy | 875 | Revise attributing distributional changes user guide entry | null | null | 2023-02-17 22:20:40+00:00 | 2023-03-07 16:14:00+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/attribute_distributional_changes.rst | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
What mechanism in my system changed between two sets of data?
For example, in a distributed computing system, we want to know why an important system metric changed in a negative w... | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
**What mechanism in my system changed between two sets of data? Or in other words, which node in my data behaves differently?**
Here we want to identify the node or nodes in the gr... | bloebp | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | It would definitely make sense, but seeing it from a perspective of someone who just wants to "quickly" look through the functionalities, I think this formulation helps more in understanding what the purpose of the method is. Otherwise, one first needs to understand what we mean by a 'mechanism' etc. | bloebp | 90 |
py-why/dowhy | 875 | Revise attributing distributional changes user guide entry | null | null | 2023-02-17 22:20:40+00:00 | 2023-03-07 16:14:00+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/attribute_distributional_changes.rst | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
What mechanism in my system changed between two sets of data?
For example, in a distributed computing system, we want to know why an important system metric changed in a negative w... | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
**What mechanism in my system changed between two sets of data? Or in other words, which node in my data behaves differently?**
Here we want to identify the node or nodes in the gr... | bloebp | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | Can we have both? One for continuity across the documentation and one for the quick reader? "What mechanism ....? Or in other words, which node in my data behaves differently?" | emrekiciman | 91 |
py-why/dowhy | 875 | Revise attributing distributional changes user guide entry | null | null | 2023-02-17 22:20:40+00:00 | 2023-03-07 16:14:00+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/attribute_distributional_changes.rst | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
What mechanism in my system changed between two sets of data?
For example, in a distributed computing system, we want to know why an important system metric changed in a negative w... | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
**What mechanism in my system changed between two sets of data? Or in other words, which node in my data behaves differently?**
Here we want to identify the node or nodes in the gr... | bloebp | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | That's a good compromise, will change it. | bloebp | 92 |
py-why/dowhy | 875 | Revise attributing distributional changes user guide entry | null | null | 2023-02-17 22:20:40+00:00 | 2023-03-07 16:14:00+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/attribute_distributional_changes.rst | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
What mechanism in my system changed between two sets of data?
For example, in a distributed computing system, we want to know why an important system metric changed in a negative w... | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
**What mechanism in my system changed between two sets of data? Or in other words, which node in my data behaves differently?**
Here we want to identify the node or nodes in the gr... | bloebp | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | uptick in latency of ___? application? | kailashbuki | 93 |
py-why/dowhy | 875 | Revise attributing distributional changes user guide entry | null | null | 2023-02-17 22:20:40+00:00 | 2023-03-07 16:14:00+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/attribute_distributional_changes.rst | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
What mechanism in my system changed between two sets of data?
For example, in a distributed computing system, we want to know why an important system metric changed in a negative w... | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
**What mechanism in my system changed between two sets of data? Or in other words, which node in my data behaves differently?**
Here we want to identify the node or nodes in the gr... | bloebp | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | It would be more accurate to say "Estimate the conditional distributions from `old` data...". | kailashbuki | 94 |
py-why/dowhy | 875 | Revise attributing distributional changes user guide entry | null | null | 2023-02-17 22:20:40+00:00 | 2023-03-07 16:14:00+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/attribute_distributional_changes.rst | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
What mechanism in my system changed between two sets of data?
For example, in a distributed computing system, we want to know why an important system metric changed in a negative w... | Attributing Distributional Changes
==================================
When attributing distribution changes, we answer the question:
**What mechanism in my system changed between two sets of data? Or in other words, which node in my data behaves differently?**
Here we want to identify the node or nodes in the gr... | bloebp | 12168ea7bd7a30d4c0d6501f69c7161ddb073845 | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | Replace old mechanisms with new one by one. | kailashbuki | 95 |
py-why/dowhy | 870 | Revise gcm user guide entry for counterfactuals | null | null | 2023-02-14 23:09:28+00:00 | 2023-03-07 16:14:22+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/computing_counterfactuals.rst | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome z for a variable Z where variable X was set to a value x. What
would have happened to the value of Z, had I intervened on X to assign it a different value x'?
As a concrete ... | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome :math:`z` for a variable :math:`Z` where variable :math:`X` was set to a value :math:`x`. What
would have happened to the value of :math:`Z`, had I intervened on :math:`X` to... | bloebp | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | d7b7cc65c5b6780fbc0a32ec4cd9f94e17878353 | 1. The data generation process can be made more realistic. Currently, there can be negative dosages too (absurd!).
2. Should we also explain what Y is? | kailashbuki | 96 |
py-why/dowhy | 870 | Revise gcm user guide entry for counterfactuals | null | null | 2023-02-14 23:09:28+00:00 | 2023-03-07 16:14:22+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/computing_counterfactuals.rst | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome z for a variable Z where variable X was set to a value x. What
would have happened to the value of Z, had I intervened on X to assign it a different value x'?
As a concrete ... | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome :math:`z` for a variable :math:`Z` where variable :math:`X` was set to a value :math:`x`. What
would have happened to the value of :math:`Z`, had I intervened on :math:`X` to... | bloebp | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | d7b7cc65c5b6780fbc0a32ec4cd9f94e17878353 | I would replace "when intervening on ..." with "had we intervened on ...". Reads better. Clearly states we are contemplating about the past. | kailashbuki | 97 |
py-why/dowhy | 870 | Revise gcm user guide entry for counterfactuals | null | null | 2023-02-14 23:09:28+00:00 | 2023-03-07 16:14:22+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/computing_counterfactuals.rst | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome z for a variable Z where variable X was set to a value x. What
would have happened to the value of Z, had I intervened on X to assign it a different value x'?
As a concrete ... | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome :math:`z` for a variable :math:`Z` where variable :math:`X` was set to a value :math:`x`. What
would have happened to the value of :math:`Z`, had I intervened on :math:`X` to... | bloebp | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | d7b7cc65c5b6780fbc0a32ec4cd9f94e17878353 | Invertibility leads to point counterfactuals. But we may also have distributional counterfactuals, where we estimate the posterior distribution of noise terms given the observational evidence. We should make the latter explicit here as most readers of Pearl are familiar with the notion of distributional counterfactuals... | kailashbuki | 98 |
py-why/dowhy | 870 | Revise gcm user guide entry for counterfactuals | null | null | 2023-02-14 23:09:28+00:00 | 2023-03-07 16:14:22+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/computing_counterfactuals.rst | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome z for a variable Z where variable X was set to a value x. What
would have happened to the value of Z, had I intervened on X to assign it a different value x'?
As a concrete ... | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome :math:`z` for a variable :math:`Z` where variable :math:`X` was set to a value :math:`x`. What
would have happened to the value of :math:`Z`, had I intervened on :math:`X` to... | bloebp | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | d7b7cc65c5b6780fbc0a32ec4cd9f94e17878353 | I completely rewrote the example to keep it connected to the cholesterol example while being somewhat 'realistic'. | bloebp | 99 |
py-why/dowhy | 870 | Revise gcm user guide entry for counterfactuals | null | null | 2023-02-14 23:09:28+00:00 | 2023-03-07 16:14:22+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/computing_counterfactuals.rst | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome z for a variable Z where variable X was set to a value x. What
would have happened to the value of Z, had I intervened on X to assign it a different value x'?
As a concrete ... | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome :math:`z` for a variable :math:`Z` where variable :math:`X` was set to a value :math:`x`. What
would have happened to the value of :math:`Z`, had I intervened on :math:`X` to... | bloebp | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | d7b7cc65c5b6780fbc0a32ec4cd9f94e17878353 | This sentence changed in the new version. | bloebp | 100 |
py-why/dowhy | 870 | Revise gcm user guide entry for counterfactuals | null | null | 2023-02-14 23:09:28+00:00 | 2023-03-07 16:14:22+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/computing_counterfactuals.rst | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome z for a variable Z where variable X was set to a value x. What
would have happened to the value of Z, had I intervened on X to assign it a different value x'?
As a concrete ... | Computing Counterfactuals
==========================
By computing counterfactuals, we answer the question:
I observed a certain outcome :math:`z` for a variable :math:`Z` where variable :math:`X` was set to a value :math:`x`. What
would have happened to the value of :math:`Z`, had I intervened on :math:`X` to... | bloebp | 5d449be765fbb04d77a35d2ace7978bfc6d90309 | d7b7cc65c5b6780fbc0a32ec4cd9f94e17878353 | Good point, I added a note at the bottom. Allowing counterfactual distributions (beyond point-wise) is anyway something high on the todo list. | bloebp | 101 |
py-why/dowhy | 867 | Revise arrow strength user guide | Signed-off-by: Patrick Bloebaum <bloebp@amazon.com> | null | 2023-02-13 21:29:38+00:00 | 2023-03-07 16:14:39+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/quantify_arrow_strength.rst | Quantifying Arrow Strength
=================================
By quantifying the strength of an arrow, we answer the question:
How strong is the causal influence from a cause to its direct effect?
How to use it
^^^^^^^^^^^^^^
To see how the method works, let us generate some data.
>>> import numpy as np, pandas... | Quantifying Arrow Strength
=================================
By quantifying the strength of an arrow, we answer the question:
**How strong is the causal influence from a cause to its direct effect?**
While there are different definitions for measuring causal influences in the literature, DoWhy offers an
implemen... | bloebp | d7b7cc65c5b6780fbc0a32ec4cd9f94e17878353 | 7ca6de528ac70ec8cb2f29d2e0389149cec0090a | `the library` -> `DoWhy` | petergtz | 102 |
py-why/dowhy | 867 | Revise arrow strength user guide | Signed-off-by: Patrick Bloebaum <bloebp@amazon.com> | null | 2023-02-13 21:29:38+00:00 | 2023-03-07 16:14:39+00:00 | docs/source/user_guide/gcm_based_inference/answering_causal_questions/quantify_arrow_strength.rst | Quantifying Arrow Strength
=================================
By quantifying the strength of an arrow, we answer the question:
How strong is the causal influence from a cause to its direct effect?
How to use it
^^^^^^^^^^^^^^
To see how the method works, let us generate some data.
>>> import numpy as np, pandas... | Quantifying Arrow Strength
=================================
By quantifying the strength of an arrow, we answer the question:
**How strong is the causal influence from a cause to its direct effect?**
While there are different definitions for measuring causal influences in the literature, DoWhy offers an
implemen... | bloebp | d7b7cc65c5b6780fbc0a32ec4cd9f94e17878353 | 7ca6de528ac70ec8cb2f29d2e0389149cec0090a | Nit: I would leave out "as mentioned before". | kailashbuki | 103 |
py-why/dowhy | 846 | Enhancement: warn about unobserved graph variables in `causal_model.identify_effect`. | Closes #810.
Introduces a `UserWarning` that is emitted if there are any graph variables that are not contained in the observed data (`self._data`). Adds a unit test for this implementation.
Currently, the return values of `self.get_common_causes()`, `self.get_instruments()` and `self.get_effect_modifiers()` are... | null | 2023-02-04 14:44:46+00:00 | 2023-02-14 20:21:29+00:00 | dowhy/causal_model.py | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | """ Module containing the main model class for the dowhy package.
"""
import logging
import typing
import warnings
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as grap... | MFreidank | 2a2b3f4f7d02f8157d4b2ea39588b305e048eca8 | 23214a9850544780e21f0afecb390446ceca48a2 | From the wording, it's not clear whether this is an ok or not ok thing. To resolve this, I'd suggest giving a little more guidance. For larger graphs, we might also want to add counts too.
Maybe something like: "The graph defines N variables. K were found in the dataset and will be analyzed as observed variables... | emrekiciman | 104 |
py-why/dowhy | 846 | Enhancement: warn about unobserved graph variables in `causal_model.identify_effect`. | Closes #810.
Introduces a `UserWarning` that is emitted if there are any graph variables that are not contained in the observed data (`self._data`). Adds a unit test for this implementation.
Currently, the return values of `self.get_common_causes()`, `self.get_instruments()` and `self.get_effect_modifiers()` are... | null | 2023-02-04 14:44:46+00:00 | 2023-02-14 20:21:29+00:00 | dowhy/causal_model.py | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | """ Module containing the main model class for the dowhy package.
"""
import logging
import typing
import warnings
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as grap... | MFreidank | 2a2b3f4f7d02f8157d4b2ea39588b305e048eca8 | 23214a9850544780e21f0afecb390446ceca48a2 | I think you could check all variables by calling causalgraph.py CausalGraph::get_all_nodes(...)
| emrekiciman | 105 |
py-why/dowhy | 846 | Enhancement: warn about unobserved graph variables in `causal_model.identify_effect`. | Closes #810.
Introduces a `UserWarning` that is emitted if there are any graph variables that are not contained in the observed data (`self._data`). Adds a unit test for this implementation.
Currently, the return values of `self.get_common_causes()`, `self.get_instruments()` and `self.get_effect_modifiers()` are... | null | 2023-02-04 14:44:46+00:00 | 2023-02-14 20:21:29+00:00 | dowhy/causal_model.py | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | """ Module containing the main model class for the dowhy package.
"""
import logging
import typing
import warnings
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as grap... | MFreidank | 2a2b3f4f7d02f8157d4b2ea39588b305e048eca8 | 23214a9850544780e21f0afecb390446ceca48a2 | Regarding your comment about whether the warnings should be visible or not by default ... how about we have a short message that says "N variables are assumed unobserved because they are not in the dataset. Pass/set the ____ flag for additional details". | emrekiciman | 106 |
py-why/dowhy | 846 | Enhancement: warn about unobserved graph variables in `causal_model.identify_effect`. | Closes #810.
Introduces a `UserWarning` that is emitted if there are any graph variables that are not contained in the observed data (`self._data`). Adds a unit test for this implementation.
Currently, the return values of `self.get_common_causes()`, `self.get_instruments()` and `self.get_effect_modifiers()` are... | null | 2023-02-04 14:44:46+00:00 | 2023-02-14 20:21:29+00:00 | dowhy/causal_model.py | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | """ Module containing the main model class for the dowhy package.
"""
import logging
import typing
import warnings
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as grap... | MFreidank | 2a2b3f4f7d02f8157d4b2ea39588b305e048eca8 | 23214a9850544780e21f0afecb390446ceca48a2 | I've tried to adapt the logging to resolve your comments. There are now two levels to the output:
* A high level warning, emitted using `warnings.warn` that points out the issue and suggests additional details are available when configuring `logging` levels.
* A lower level, more detailed warning, emitted using `self... | MFreidank | 107 |
py-why/dowhy | 846 | Enhancement: warn about unobserved graph variables in `causal_model.identify_effect`. | Closes #810.
Introduces a `UserWarning` that is emitted if there are any graph variables that are not contained in the observed data (`self._data`). Adds a unit test for this implementation.
Currently, the return values of `self.get_common_causes()`, `self.get_instruments()` and `self.get_effect_modifiers()` are... | null | 2023-02-04 14:44:46+00:00 | 2023-02-14 20:21:29+00:00 | dowhy/causal_model.py | """ Module containing the main model class for the dowhy package.
"""
import logging
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as graph_learners
import dowhy.utils.... | """ Module containing the main model class for the dowhy package.
"""
import logging
import typing
import warnings
from itertools import combinations
from sympy import init_printing
import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.graph_learners as grap... | MFreidank | 2a2b3f4f7d02f8157d4b2ea39588b305e048eca8 | 23214a9850544780e21f0afecb390446ceca48a2 | That's easier indeed, thanks for pointing this out.
Resolved now. | MFreidank | 108 |
py-why/dowhy | 846 | Enhancement: warn about unobserved graph variables in `causal_model.identify_effect`. | Closes #810.
Introduces a `UserWarning` that is emitted if there are any graph variables that are not contained in the observed data (`self._data`). Adds a unit test for this implementation.
Currently, the return values of `self.get_common_causes()`, `self.get_instruments()` and `self.get_effect_modifiers()` are... | null | 2023-02-04 14:44:46+00:00 | 2023-02-14 20:21:29+00:00 | tests/test_causal_model.py | import pandas as pd
import pytest
from pytest import mark
from sklearn import linear_model
import dowhy
import dowhy.datasets
from dowhy import CausalModel
class TestCausalModel(object):
@mark.parametrize(
["beta", "num_samples", "num_treatments"],
[
(10, 100, 1),
],
)
... | import pandas as pd
import pytest
from pytest import mark
from sklearn import linear_model
import dowhy
import dowhy.datasets
from dowhy import CausalModel
class TestCausalModel(object):
@mark.parametrize(
["beta", "num_samples", "num_treatments"],
[
(10, 100, 1),
],
)
... | MFreidank | 2a2b3f4f7d02f8157d4b2ea39588b305e048eca8 | 23214a9850544780e21f0afecb390446ceca48a2 | Were these whitespace edits intentional? | emrekiciman | 109 |
py-why/dowhy | 846 | Enhancement: warn about unobserved graph variables in `causal_model.identify_effect`. | Closes #810.
Introduces a `UserWarning` that is emitted if there are any graph variables that are not contained in the observed data (`self._data`). Adds a unit test for this implementation.
Currently, the return values of `self.get_common_causes()`, `self.get_instruments()` and `self.get_effect_modifiers()` are... | null | 2023-02-04 14:44:46+00:00 | 2023-02-14 20:21:29+00:00 | tests/test_causal_model.py | import pandas as pd
import pytest
from pytest import mark
from sklearn import linear_model
import dowhy
import dowhy.datasets
from dowhy import CausalModel
class TestCausalModel(object):
@mark.parametrize(
["beta", "num_samples", "num_treatments"],
[
(10, 100, 1),
],
)
... | import pandas as pd
import pytest
from pytest import mark
from sklearn import linear_model
import dowhy
import dowhy.datasets
from dowhy import CausalModel
class TestCausalModel(object):
@mark.parametrize(
["beta", "num_samples", "num_treatments"],
[
(10, 100, 1),
],
)
... | MFreidank | 2a2b3f4f7d02f8157d4b2ea39588b305e048eca8 | 23214a9850544780e21f0afecb390446ceca48a2 | I believe this is causing the format check to fail in the build process. | emrekiciman | 110 |
py-why/dowhy | 846 | Enhancement: warn about unobserved graph variables in `causal_model.identify_effect`. | Closes #810.
Introduces a `UserWarning` that is emitted if there are any graph variables that are not contained in the observed data (`self._data`). Adds a unit test for this implementation.
Currently, the return values of `self.get_common_causes()`, `self.get_instruments()` and `self.get_effect_modifiers()` are... | null | 2023-02-04 14:44:46+00:00 | 2023-02-14 20:21:29+00:00 | tests/test_causal_model.py | import pandas as pd
import pytest
from pytest import mark
from sklearn import linear_model
import dowhy
import dowhy.datasets
from dowhy import CausalModel
class TestCausalModel(object):
@mark.parametrize(
["beta", "num_samples", "num_treatments"],
[
(10, 100, 1),
],
)
... | import pandas as pd
import pytest
from pytest import mark
from sklearn import linear_model
import dowhy
import dowhy.datasets
from dowhy import CausalModel
class TestCausalModel(object):
@mark.parametrize(
["beta", "num_samples", "num_treatments"],
[
(10, 100, 1),
],
)
... | MFreidank | 2a2b3f4f7d02f8157d4b2ea39588b305e048eca8 | 23214a9850544780e21f0afecb390446ceca48a2 | No, they weren't intentional. Will look into fixing this, might have been caused by some editor auto-formatting. | MFreidank | 111 |
py-why/dowhy | 838 | Rewriting the User Guide | Hi all,
This is a first stab at unifying the user guide for effect estimation and gcm sub-package. This is an extremely early draft and will require a lot of iterations before we can put it into the right place. I'm already publishing it here to avoid going in the wrong direction and wasting time. To simplify iterat... | null | 2023-01-27 15:39:34+00:00 | 2023-07-27 18:47:39+00:00 | docs/source/user_guide/intro.rst | Introduction to DoWhy
=====================
The need for causal inference
----------------------------------
Predictive models uncover patterns that connect the inputs and outcome in observed data. To intervene, however, we need to estimate the effect of changing an input from its current value, for which no data exi... | Introduction to DoWhy
=====================
Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, causal structure learn... | petergtz | ccc4af28684f251d457c740d502f1e3e0da327fd | 0df1f4c5d2f737dd70bacfaf00058b1da1d5dbf9 | Can also link the examples here. Maybe something along the line of "See also the example notebooks[Link] how to use them in a concrete problem." | bloebp | 112 |
py-why/dowhy | 838 | Rewriting the User Guide | Hi all,
This is a first stab at unifying the user guide for effect estimation and gcm sub-package. This is an extremely early draft and will require a lot of iterations before we can put it into the right place. I'm already publishing it here to avoid going in the wrong direction and wasting time. To simplify iterat... | null | 2023-01-27 15:39:34+00:00 | 2023-07-27 18:47:39+00:00 | docs/source/user_guide/intro.rst | Introduction to DoWhy
=====================
The need for causal inference
----------------------------------
Predictive models uncover patterns that connect the inputs and outcome in observed data. To intervene, however, we need to estimate the effect of changing an input from its current value, for which no data exi... | Introduction to DoWhy
=====================
Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, causal structure learn... | petergtz | ccc4af28684f251d457c740d502f1e3e0da327fd | 0df1f4c5d2f737dd70bacfaf00058b1da1d5dbf9 | Suggestion:
To perform tasks, DoWhy leverages two powerful frameworks, namely graphical causal models (GCM) and potential outcomes (PO), depending on the task at hand. What’s common to most tasks is that they require a causal graph, which is modeled after the problem domain. For that reason, this user guide starts wit... | bloebp | 113 |
py-why/dowhy | 838 | Rewriting the User Guide | Hi all,
This is a first stab at unifying the user guide for effect estimation and gcm sub-package. This is an extremely early draft and will require a lot of iterations before we can put it into the right place. I'm already publishing it here to avoid going in the wrong direction and wasting time. To simplify iterat... | null | 2023-01-27 15:39:34+00:00 | 2023-07-27 18:47:39+00:00 | docs/source/user_guide/intro.rst | Introduction to DoWhy
=====================
The need for causal inference
----------------------------------
Predictive models uncover patterns that connect the inputs and outcome in observed data. To intervene, however, we need to estimate the effect of changing an input from its current value, for which no data exi... | Introduction to DoWhy
=====================
Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, causal structure learn... | petergtz | ccc4af28684f251d457c740d502f1e3e0da327fd | 0df1f4c5d2f737dd70bacfaf00058b1da1d5dbf9 | Comments on the new image:
Its really great! Two things:
- Can maybe more generally say “Quantifying causal contributions” or something along the line instead of specifically ICC, seeing that we also have direct arrow strength.
- Would it make sense to add an optional validation/refutation step? Although this might ... | bloebp | 114 |
py-why/dowhy | 838 | Rewriting the User Guide | Hi all,
This is a first stab at unifying the user guide for effect estimation and gcm sub-package. This is an extremely early draft and will require a lot of iterations before we can put it into the right place. I'm already publishing it here to avoid going in the wrong direction and wasting time. To simplify iterat... | null | 2023-01-27 15:39:34+00:00 | 2023-07-27 18:47:39+00:00 | docs/source/user_guide/intro.rst | Introduction to DoWhy
=====================
The need for causal inference
----------------------------------
Predictive models uncover patterns that connect the inputs and outcome in observed data. To intervene, however, we need to estimate the effect of changing an input from its current value, for which no data exi... | Introduction to DoWhy
=====================
Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, causal structure learn... | petergtz | ccc4af28684f251d457c740d502f1e3e0da327fd | 0df1f4c5d2f737dd70bacfaf00058b1da1d5dbf9 | > Can maybe more generally say “Quantifying causal contributions” or something along the line instead of specifically ICC, seeing that we also have direct arrow strength.
Yes. The PowerPoint presentation (which acts as source for this png) is alongside everything else. Just open it, make the necessary changes and ta... | petergtz | 115 |
py-why/dowhy | 829 | Pin Numpy version to 1.23.1 | This makes it compatible with Numba version 0.56.4. The way the Numpy version was specified before, installs version 1.24.0 which is incompatible with Numba, see also https://github.com/numba/numba/pull/8691. Numba is a transient dependency coming in through `econml` and `sparse`.
The resulting error looks like this... | null | 2023-01-24 09:37:39+00:00 | 2023-01-26 16:19:34+00:00 | pyproject.toml | [tool.poetry]
name = "dowhy"
#
# 0.0.0 is standard placeholder for poetry-dynamic-versioning
# any changes to this should not be checked in
#
version = "0.0.0"
description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions"
authors = ["PyWhy Community <a... | [tool.poetry]
name = "dowhy"
#
# 0.0.0 is standard placeholder for poetry-dynamic-versioning
# any changes to this should not be checked in
#
version = "0.0.0"
description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions"
authors = ["PyWhy Community <a... | petergtz | 96a055ae4448ecddeb458d86b10588ea3295c4a3 | ca340303e993fa4be1831f74afccf9161e19a9d8 | I don't think the `==` is strictly necessary here. With semver you can just remove the `^` symbol to match the explicit version. | darthtrevino | 116 |
py-why/dowhy | 811 | Enhancement/remove data from estimators | * Remove data, treatment and outcome from estimator object
* estimate_effect now have data, treatment and outcome as parameters | null | 2023-01-03 18:39:35+00:00 | 2023-01-20 11:06:39+00:00 | dowhy/causal_estimator.py | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | import copy
import logging
from collections import namedtuple
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier... | andresmor-ms | 325cf4e245de3e55b85a42c5fefc36f6ef34db46 | c3e1c75696a8297aa48977ea4c7b7e8dc50dc6aa | do we require treatment_name as a parameter here since it can be inferred from self?
In general, I think it is okay for internal methods that start with _ to require this argument, but definitely user-facing methods can avoid it. | amit-sharma | 117 |
py-why/dowhy | 811 | Enhancement/remove data from estimators | * Remove data, treatment and outcome from estimator object
* estimate_effect now have data, treatment and outcome as parameters | null | 2023-01-03 18:39:35+00:00 | 2023-01-20 11:06:39+00:00 | dowhy/causal_estimator.py | import copy
import logging
from collections import namedtuple
from typing import Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand... | import copy
import logging
from collections import namedtuple
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
import sympy as sp
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.causal_identifier... | andresmor-ms | 325cf4e245de3e55b85a42c5fefc36f6ef34db46 | c3e1c75696a8297aa48977ea4c7b7e8dc50dc6aa | same comment, I suggest removing the treatment_name argument. | amit-sharma | 118 |
py-why/dowhy | 811 | Enhancement/remove data from estimators | * Remove data, treatment and outcome from estimator object
* estimate_effect now have data, treatment and outcome as parameters | null | 2023-01-03 18:39:35+00:00 | 2023-01-20 11:06:39+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
from typing import Any, Callable, List, Optional, Protocol, Union
from warnings import warn
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_i... | import inspect
from importlib import import_module
from typing import Any, Callable, List, Optional, Protocol, Union
from warnings import warn
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_i... | andresmor-ms | 325cf4e245de3e55b85a42c5fefc36f6ef34db46 | c3e1c75696a8297aa48977ea4c7b7e8dc50dc6aa | we might not need treatment_name | amit-sharma | 119 |
py-why/dowhy | 811 | Enhancement/remove data from estimators | * Remove data, treatment and outcome from estimator object
* estimate_effect now have data, treatment and outcome as parameters | null | 2023-01-03 18:39:35+00:00 | 2023-01-20 11:06:39+00:00 | dowhy/causal_estimators/generalized_linear_model_estimator.py | import itertools
from typing import Any, List, Optional, Union
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimator
from dowhy.causal_estimators.regression_estimator import RegressionEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class GeneralizedLine... | import itertools
from typing import Any, List, Optional, Union
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimator
from dowhy.causal_estimators.regression_estimator import RegressionEstimator
from dowhy.causal_identifier import IdentifiedEstimand
class GeneralizedLine... | andresmor-ms | 325cf4e245de3e55b85a42c5fefc36f6ef34db46 | c3e1c75696a8297aa48977ea4c7b7e8dc50dc6aa | treatment and outcome names can be avoided for predict_fn and _build_model. | amit-sharma | 120 |
py-why/dowhy | 811 | Enhancement/remove data from estimators | * Remove data, treatment and outcome from estimator object
* estimate_effect now have data, treatment and outcome as parameters | null | 2023-01-03 18:39:35+00:00 | 2023-01-20 11:06:39+00:00 | dowhy/causal_estimators/instrumental_variable_estimator.py | from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.causal_identifier import Ide... | from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sympy as sp
import sympy.stats as spstats
from statsmodels.sandbox.regression.gmm import IV2SLS
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, RealizedEstimand
from dowhy.causal_identifier import Ide... | andresmor-ms | 325cf4e245de3e55b85a42c5fefc36f6ef34db46 | c3e1c75696a8297aa48977ea4c7b7e8dc50dc6aa | can avoid treatment_name and outcome_name here too | amit-sharma | 121 |
py-why/dowhy | 811 | Enhancement/remove data from estimators | * Remove data, treatment and outcome from estimator object
* estimate_effect now have data, treatment and outcome as parameters | null | 2023-01-03 18:39:35+00:00 | 2023-01-20 11:06:39+00:00 | dowhy/causal_estimators/regression_estimator.py | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, IdentifiedEstimand
class RegressionEstimator(CausalEstimator):
"""Compute effect of treatment using some regression function.
... | from typing import Any, List, Optional, Union
import numpy as np
import pandas as pd
import statsmodels.api as sm
from dowhy.causal_estimator import CausalEstimate, CausalEstimator, IdentifiedEstimand
class RegressionEstimator(CausalEstimator):
"""Compute effect of treatment using some regression function.
... | andresmor-ms | 325cf4e245de3e55b85a42c5fefc36f6ef34db46 | c3e1c75696a8297aa48977ea4c7b7e8dc50dc6aa | treatment_name can be avoided here too as a parameter | amit-sharma | 122 |
py-why/dowhy | 811 | Enhancement/remove data from estimators | * Remove data, treatment and outcome from estimator object
* estimate_effect now have data, treatment and outcome as parameters | null | 2023-01-03 18:39:35+00:00 | 2023-01-20 11:06:39+00:00 | dowhy/causal_estimators/two_stage_regression_estimator.py | import copy
from typing import Any, List, Optional, Type, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier import EstimandType, Identifi... | import copy
from typing import Any, List, Optional, Type, Union
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
from dowhy.causal_identifier import EstimandType, Identifi... | andresmor-ms | 325cf4e245de3e55b85a42c5fefc36f6ef34db46 | c3e1c75696a8297aa48977ea4c7b7e8dc50dc6aa | treatment name parameter can be avoided here too | amit-sharma | 123 |
py-why/dowhy | 789 | Add Python 3.10 support | * Add Python version condition on `autogluon-tabular`.
* Remove `autogluon-tabular` from dev-dependencies to add Python 3.10 to our builds.
**Note**: `autogluon-tabular` has a dependency constraint on `scikit-learn` of `<1.2.0`. So we're able to advance `scikit-learn` past `1.0.2`, but not to the most recent versio... | null | 2022-12-13 17:13:03+00:00 | 2022-12-15 14:13:24+00:00 | pyproject.toml | [tool.poetry]
name = "dowhy"
#
# 0.0.0 is standard placeholder for poetry-dynamic-versioning
# any changes to this should not be checked in
#
version = "0.0.0"
description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions"
authors = ["PyWhy Community <a... | [tool.poetry]
name = "dowhy"
#
# 0.0.0 is standard placeholder for poetry-dynamic-versioning
# any changes to this should not be checked in
#
version = "0.0.0"
description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions"
authors = ["PyWhy Community <a... | darthtrevino | 5bbac57b8104d99b03bd5e439a7c671bc7215303 | 7c6f56fb9023b023e694beba51656d826d9c1843 | Is tensorflow still required? I thought this was only a dependency of autogluon. | petergtz | 124 |
py-why/dowhy | 789 | Add Python 3.10 support | * Add Python version condition on `autogluon-tabular`.
* Remove `autogluon-tabular` from dev-dependencies to add Python 3.10 to our builds.
**Note**: `autogluon-tabular` has a dependency constraint on `scikit-learn` of `<1.2.0`. So we're able to advance `scikit-learn` past `1.0.2`, but not to the most recent versio... | null | 2022-12-13 17:13:03+00:00 | 2022-12-15 14:13:24+00:00 | tests/gcm/ml/test_autogluon.py | import tempfile
import numpy as np
from flaky import flaky
from pytest import approx
from sklearn.model_selection import train_test_split
from dowhy.gcm.fcms import AdditiveNoiseModel, ClassifierFCM
from dowhy.gcm.ml.autolguon import AutoGluonClassifier, AutoGluonRegressor
from dowhy.gcm.util.general import shape_int... | import tempfile
import numpy as np
from flaky import flaky
from pytest import approx, importorskip, mark
from sklearn.model_selection import train_test_split
from dowhy.gcm.fcms import AdditiveNoiseModel, ClassifierFCM
autolguon = importorskip("dowhy.gcm.ml.autolguon")
from dowhy.gcm.ml.autolguon import AutoGluonCla... | darthtrevino | 5bbac57b8104d99b03bd5e439a7c671bc7215303 | 7c6f56fb9023b023e694beba51656d826d9c1843 | I think for this there's a better way to do it. Reading https://docs.pytest.org/en/7.1.x/how-to/skipping.html#skipping-on-a-missing-import-dependency it should be possible to do:
```python
autolguon = pytest.importorskip("dowhy.gcm.ml.autolguon")
from dowhy.gcm.ml.autolguon import AutoGluonClassifier, AutoGluonReg... | petergtz | 125 |
py-why/dowhy | 789 | Add Python 3.10 support | * Add Python version condition on `autogluon-tabular`.
* Remove `autogluon-tabular` from dev-dependencies to add Python 3.10 to our builds.
**Note**: `autogluon-tabular` has a dependency constraint on `scikit-learn` of `<1.2.0`. So we're able to advance `scikit-learn` past `1.0.2`, but not to the most recent versio... | null | 2022-12-13 17:13:03+00:00 | 2022-12-15 14:13:24+00:00 | tests/gcm/test_auto.py | import networkx as nx
import numpy as np
import pandas as pd
from flaky import flaky
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
from sklearn.naive_bayes import GaussianNB... | import networkx as nx
import numpy as np
import pandas as pd
from flaky import flaky
from pytest import mark
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
from sklearn.naive... | darthtrevino | 5bbac57b8104d99b03bd5e439a7c671bc7215303 | 7c6f56fb9023b023e694beba51656d826d9c1843 | With the skip marker, is it really necessary to comment out the code? Is PyTest not entirely skipping this test? Assuming that, we can simply move the `AutoGluonClassifier` import into the test function itself.
I just pushed a new commit trying this out. | petergtz | 126 |
py-why/dowhy | 789 | Add Python 3.10 support | * Add Python version condition on `autogluon-tabular`.
* Remove `autogluon-tabular` from dev-dependencies to add Python 3.10 to our builds.
**Note**: `autogluon-tabular` has a dependency constraint on `scikit-learn` of `<1.2.0`. So we're able to advance `scikit-learn` past `1.0.2`, but not to the most recent versio... | null | 2022-12-13 17:13:03+00:00 | 2022-12-15 14:13:24+00:00 | tests/gcm/test_auto.py | import networkx as nx
import numpy as np
import pandas as pd
from flaky import flaky
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
from sklearn.naive_bayes import GaussianNB... | import networkx as nx
import numpy as np
import pandas as pd
from flaky import flaky
from pytest import mark
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
from sklearn.naive... | darthtrevino | 5bbac57b8104d99b03bd5e439a7c671bc7215303 | 7c6f56fb9023b023e694beba51656d826d9c1843 | I think the reasoning was that the import would fail, but let's see what it does | darthtrevino | 127 |
py-why/dowhy | 789 | Add Python 3.10 support | * Add Python version condition on `autogluon-tabular`.
* Remove `autogluon-tabular` from dev-dependencies to add Python 3.10 to our builds.
**Note**: `autogluon-tabular` has a dependency constraint on `scikit-learn` of `<1.2.0`. So we're able to advance `scikit-learn` past `1.0.2`, but not to the most recent versio... | null | 2022-12-13 17:13:03+00:00 | 2022-12-15 14:13:24+00:00 | tests/gcm/test_auto.py | import networkx as nx
import numpy as np
import pandas as pd
from flaky import flaky
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
from sklearn.naive_bayes import GaussianNB... | import networkx as nx
import numpy as np
import pandas as pd
from flaky import flaky
from pytest import mark
from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV
from sklearn.naive... | darthtrevino | 5bbac57b8104d99b03bd5e439a7c671bc7215303 | 7c6f56fb9023b023e694beba51656d826d9c1843 | LGTM! | darthtrevino | 128 |
py-why/dowhy | 786 | Make autogluon optional | It looks like since this has not been listed in the 'extra' group, it's not marked as optional.
Right now, a `pip install dowhy` pulls in autogluon and pytorch and makes this a pretty large installation. I believe this change should fix this.
Signed-off-by: Peter Goetz <pego@amazon.com> | null | 2022-12-09 22:26:14+00:00 | 2022-12-12 09:07:24+00:00 | pyproject.toml | [tool.poetry]
name = "dowhy"
#
# 0.0.0 is standard placeholder for poetry-dynamic-versioning
# any changes to this should not be checked in
#
version = "0.0.0"
description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions"
authors = ["PyWhy Community <a... | [tool.poetry]
name = "dowhy"
#
# 0.0.0 is standard placeholder for poetry-dynamic-versioning
# any changes to this should not be checked in
#
version = "0.0.0"
description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions"
authors = ["PyWhy Community <a... | petergtz | 6ebfc4464bd5c41880f25299df25c3c1cd05b20c | b5ae49a256217f1cae5a45033e8e6bc84599e508 | I think the package name is autogluon.tabular. | bloebp | 129 |
py-why/dowhy | 786 | Make autogluon optional | It looks like since this has not been listed in the 'extra' group, it's not marked as optional.
Right now, a `pip install dowhy` pulls in autogluon and pytorch and makes this a pretty large installation. I believe this change should fix this.
Signed-off-by: Peter Goetz <pego@amazon.com> | null | 2022-12-09 22:26:14+00:00 | 2022-12-12 09:07:24+00:00 | pyproject.toml | [tool.poetry]
name = "dowhy"
#
# 0.0.0 is standard placeholder for poetry-dynamic-versioning
# any changes to this should not be checked in
#
version = "0.0.0"
description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions"
authors = ["PyWhy Community <a... | [tool.poetry]
name = "dowhy"
#
# 0.0.0 is standard placeholder for poetry-dynamic-versioning
# any changes to this should not be checked in
#
version = "0.0.0"
description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions"
authors = ["PyWhy Community <a... | petergtz | 6ebfc4464bd5c41880f25299df25c3c1cd05b20c | b5ae49a256217f1cae5a45033e8e6bc84599e508 | They both work. According to https://peps.python.org/pep-0503/#normalized-names, a dot gets replaced by a dash. Note also that we use `autogluon-tabular` already in other places in this project file. Also tested this locally. Both names work. | petergtz | 130 |
py-why/dowhy | 778 | [Version Bump v0.9] Release notes and updated docstrings | This PR includes the release notes for v0.9
Signed-off-by: Amit Sharma <amit_sharma@live.com> | null | 2022-12-04 10:24:21+00:00 | 2022-12-06 05:37:44+00:00 | docs/source/code_repo.rst | Release notes
=============
DoWhy is hosted on GitHub.
You can browse the code in a html-friendly format `here
<https://github.com/Microsoft/dowhy>`_.
v0.7.1: Added Graph refuter. Support for dagitty graphs and external estimators
--------------------------------------------------------------------------------------... | Release notes
=============
DoWhy is hosted on GitHub.
You can browse the code in a html-friendly format `here
<https://github.com/Microsoft/dowhy>`_.
v0.9: New functional API (preview), faster refutations, and better independence tests for GCMs
-----------------------------------------------------------------------... | amit-sharma | 649b3c32a11ddff86e932c24b3551161ba5f5747 | 9e938f333ec5436697a4f48a7ebb0e63a39a0303 | This should also list:
```
* New API for unit change attribution (@kailashbuki)
* New quality option `BEST` for auto-assignment of causal mechanisms, which uses the optional auto-ML library AutoGluon (@bloebp)
``` | petergtz | 131 |
py-why/dowhy | 778 | [Version Bump v0.9] Release notes and updated docstrings | This PR includes the release notes for v0.9
Signed-off-by: Amit Sharma <amit_sharma@live.com> | null | 2022-12-04 10:24:21+00:00 | 2022-12-06 05:37:44+00:00 | docs/source/code_repo.rst | Release notes
=============
DoWhy is hosted on GitHub.
You can browse the code in a html-friendly format `here
<https://github.com/Microsoft/dowhy>`_.
v0.7.1: Added Graph refuter. Support for dagitty graphs and external estimators
--------------------------------------------------------------------------------------... | Release notes
=============
DoWhy is hosted on GitHub.
You can browse the code in a html-friendly format `here
<https://github.com/Microsoft/dowhy>`_.
v0.9: New functional API (preview), faster refutations, and better independence tests for GCMs
-----------------------------------------------------------------------... | amit-sharma | 649b3c32a11ddff86e932c24b3551161ba5f5747 | 9e938f333ec5436697a4f48a7ebb0e63a39a0303 | > Also, you can now browse docs separately for different versions of DoWhy
This was already available in the previous version, so you can probably remove that.
How about:
```
* New PyData theme for documentation with new homepage, Getting started guide, revised User Guide and examples page (@petergtz)
```
... | petergtz | 132 |
py-why/dowhy | 778 | [Version Bump v0.9] Release notes and updated docstrings | This PR includes the release notes for v0.9
Signed-off-by: Amit Sharma <amit_sharma@live.com> | null | 2022-12-04 10:24:21+00:00 | 2022-12-06 05:37:44+00:00 | docs/source/code_repo.rst | Release notes
=============
DoWhy is hosted on GitHub.
You can browse the code in a html-friendly format `here
<https://github.com/Microsoft/dowhy>`_.
v0.7.1: Added Graph refuter. Support for dagitty graphs and external estimators
--------------------------------------------------------------------------------------... | Release notes
=============
DoWhy is hosted on GitHub.
You can browse the code in a html-friendly format `here
<https://github.com/Microsoft/dowhy>`_.
v0.9: New functional API (preview), faster refutations, and better independence tests for GCMs
-----------------------------------------------------------------------... | amit-sharma | 649b3c32a11ddff86e932c24b3551161ba5f5747 | 9e938f333ec5436697a4f48a7ebb0e63a39a0303 | Got it, making those changes. | amit-sharma | 133 |
py-why/dowhy | 778 | [Version Bump v0.9] Release notes and updated docstrings | This PR includes the release notes for v0.9
Signed-off-by: Amit Sharma <amit_sharma@live.com> | null | 2022-12-04 10:24:21+00:00 | 2022-12-06 05:37:44+00:00 | docs/source/dowhy.rst | API reference
=============
Subpackages
-----------
.. toctree::
:maxdepth: 4
dowhy.api
dowhy.causal_estimators
dowhy.causal_identifiers
dowhy.causal_refuters
dowhy.data_transformers
dowhy.do_samplers
dowhy.gcm
dowhy.graph_learners
dowhy.interpreters
dowhy.utils
Submodules
---------... | API reference
=============
Subpackages
-----------
.. toctree::
:maxdepth: 4
dowhy.api
dowhy.causal_estimators
dowhy.causal_identifier
dowhy.causal_refuters
dowhy.data_transformers
dowhy.do_samplers
dowhy.gcm
dowhy.graph_learners
dowhy.interpreters
dowhy.utils
Submodules
----------... | amit-sharma | 649b3c32a11ddff86e932c24b3551161ba5f5747 | 9e938f333ec5436697a4f48a7ebb0e63a39a0303 | Did you change this on purpose? Typically, this part is called the "API Reference", so I would assume users would look for that when they search for documentation on DoWhy's API. | petergtz | 134 |
py-why/dowhy | 778 | [Version Bump v0.9] Release notes and updated docstrings | This PR includes the release notes for v0.9
Signed-off-by: Amit Sharma <amit_sharma@live.com> | null | 2022-12-04 10:24:21+00:00 | 2022-12-06 05:37:44+00:00 | docs/source/dowhy.rst | API reference
=============
Subpackages
-----------
.. toctree::
:maxdepth: 4
dowhy.api
dowhy.causal_estimators
dowhy.causal_identifiers
dowhy.causal_refuters
dowhy.data_transformers
dowhy.do_samplers
dowhy.gcm
dowhy.graph_learners
dowhy.interpreters
dowhy.utils
Submodules
---------... | API reference
=============
Subpackages
-----------
.. toctree::
:maxdepth: 4
dowhy.api
dowhy.causal_estimators
dowhy.causal_identifier
dowhy.causal_refuters
dowhy.data_transformers
dowhy.do_samplers
dowhy.gcm
dowhy.graph_learners
dowhy.interpreters
dowhy.utils
Submodules
----------... | amit-sharma | 649b3c32a11ddff86e932c24b3551161ba5f5747 | 9e938f333ec5436697a4f48a7ebb0e63a39a0303 | No, let me revert it back. | amit-sharma | 135 |
py-why/dowhy | 768 | An attempt at a DCO-compliant version of multivalue treatment PR | Signed-off-by: Egor Kraev egor.kraev@wise.com | null | 2022-11-21 08:19:21+00:00 | 2022-11-24 06:17:03+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
import econml
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wrapper class for estimators from the EconML library.
For a li... | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | EgorKraevTransferwise | f83a276393f7f89e8c7991e5750c2f1095127e04 | 6cf8366c48b5f010952c14f615585a41cd37c3bf | what does tt stand for? true treatment? | amit-sharma | 136 |
py-why/dowhy | 768 | An attempt at a DCO-compliant version of multivalue treatment PR | Signed-off-by: Egor Kraev egor.kraev@wise.com | null | 2022-11-21 08:19:21+00:00 | 2022-11-24 06:17:03+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
import econml
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wrapper class for estimators from the EconML library.
For a li... | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | EgorKraevTransferwise | f83a276393f7f89e8c7991e5750c2f1095127e04 | 6cf8366c48b5f010952c14f615585a41cd37c3bf | I did not understand what this loop does? An inline comment will help. | amit-sharma | 137 |
py-why/dowhy | 768 | An attempt at a DCO-compliant version of multivalue treatment PR | Signed-off-by: Egor Kraev egor.kraev@wise.com | null | 2022-11-21 08:19:21+00:00 | 2022-11-24 06:17:03+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
import econml
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wrapper class for estimators from the EconML library.
For a li... | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | EgorKraevTransferwise | f83a276393f7f89e8c7991e5750c2f1095127e04 | 6cf8366c48b5f010952c14f615585a41cd37c3bf | will it possible to add a docstring for these methods? It will be good to add a docstring to the the methods we expect people to access. In the past, we were not so consistent, but this will be a good practice. | amit-sharma | 138 |
py-why/dowhy | 768 | An attempt at a DCO-compliant version of multivalue treatment PR | Signed-off-by: Egor Kraev egor.kraev@wise.com | null | 2022-11-21 08:19:21+00:00 | 2022-11-24 06:17:03+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
import econml
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wrapper class for estimators from the EconML library.
For a li... | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | EgorKraevTransferwise | f83a276393f7f89e8c7991e5750c2f1095127e04 | 6cf8366c48b5f010952c14f615585a41cd37c3bf | Adding a line in docstring to clarify | EgorKraevTransferwise | 139 |
py-why/dowhy | 768 | An attempt at a DCO-compliant version of multivalue treatment PR | Signed-off-by: Egor Kraev egor.kraev@wise.com | null | 2022-11-21 08:19:21+00:00 | 2022-11-24 06:17:03+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
import econml
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wrapper class for estimators from the EconML library.
For a li... | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | EgorKraevTransferwise | f83a276393f7f89e8c7991e5750c2f1095127e04 | 6cf8366c48b5f010952c14f615585a41cd37c3bf | Adding | EgorKraevTransferwise | 140 |
py-why/dowhy | 768 | An attempt at a DCO-compliant version of multivalue treatment PR | Signed-off-by: Egor Kraev egor.kraev@wise.com | null | 2022-11-21 08:19:21+00:00 | 2022-11-24 06:17:03+00:00 | dowhy/causal_estimators/econml.py | import inspect
from importlib import import_module
import econml
import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wrapper class for estimators from the EconML library.
For a li... | import inspect
from importlib import import_module
from typing import Callable
import numpy as np
import pandas as pd
from numpy.distutils.misc_util import is_sequence
from dowhy.causal_estimator import CausalEstimate, CausalEstimator
from dowhy.utils.api import parse_state
class Econml(CausalEstimator):
"""Wra... | EgorKraevTransferwise | f83a276393f7f89e8c7991e5750c2f1095127e04 | 6cf8366c48b5f010952c14f615585a41cd37c3bf | Adding | EgorKraevTransferwise | 141 |
py-why/dowhy | 766 | Adding autogluon as optional dependency to gcm module | This extends the auto model assignment by the 'BEST' parameter, which returns an autogluon model (i.e., an auto ML model). | null | 2022-11-18 00:33:18+00:00 | 2022-11-23 21:05:13+00:00 | poetry.lock | [[package]]
name = "absl-py"
version = "1.3.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
category = "dev"
optional = false
python-versions = ">=3.6"
[[package]]
name = "alabaster"
version = "0.7.12"
description = "A configurable sidebar-enabled Sphinx theme"
category = "m... | [[package]]
name = "absl-py"
version = "1.3.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
category = "dev"
optional = false
python-versions = ">=3.6"
[[package]]
name = "alabaster"
version = "0.7.12"
description = "A configurable sidebar-enabled Sphinx theme"
category = "m... | bloebp | 0a03cb10baddae6d6ca94d4d38fa074f5391b426 | f83a276393f7f89e8c7991e5750c2f1095127e04 | Please make sure you're on the latest poetry version. I believe latest uses underscores, not dashes. Let's try and keep noise in the lock as low as possible. | petergtz | 142 |
py-why/dowhy | 766 | Adding autogluon as optional dependency to gcm module | This extends the auto model assignment by the 'BEST' parameter, which returns an autogluon model (i.e., an auto ML model). | null | 2022-11-18 00:33:18+00:00 | 2022-11-23 21:05:13+00:00 | poetry.lock | [[package]]
name = "absl-py"
version = "1.3.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
category = "dev"
optional = false
python-versions = ">=3.6"
[[package]]
name = "alabaster"
version = "0.7.12"
description = "A configurable sidebar-enabled Sphinx theme"
category = "m... | [[package]]
name = "absl-py"
version = "1.3.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
category = "dev"
optional = false
python-versions = ">=3.6"
[[package]]
name = "alabaster"
version = "0.7.12"
description = "A configurable sidebar-enabled Sphinx theme"
category = "m... | bloebp | 0a03cb10baddae6d6ca94d4d38fa074f5391b426 | f83a276393f7f89e8c7991e5750c2f1095127e04 | Sorted via Discord. Looks like the latest version of Poetry (1.2.2) actually does use dashes. Hopefully this will not be an issue anymore in the future. | petergtz | 143 |
py-why/dowhy | 764 | Add convenience function to plot attribution scores | We're using function in so many places in notebooks, similar to the graph plot utility that I think it's worth providing this as a convenience function. | null | 2022-11-15 22:35:03+00:00 | 2022-11-16 14:11:16+00:00 | dowhy/gcm/util/plotting.py | import logging
from typing import Any, Dict, List, Optional, Tuple
import networkx as nx
import pandas as pd
from matplotlib import pyplot
from networkx.drawing import nx_pydot
_logger = logging.getLogger(__name__)
def plot(
causal_graph: nx.Graph,
causal_strengths: Optional[Dict[Tuple[Any, Any], float]] = ... | import logging
from typing import Any, Dict, List, Optional, Tuple
import networkx as nx
import numpy as np
import pandas as pd
from matplotlib import pyplot
from networkx.drawing import nx_pydot
_logger = logging.getLogger(__name__)
def plot(
causal_graph: nx.Graph,
causal_strengths: Optional[Dict[Tuple[An... | petergtz | 7568d9528b7108830821e8cc729c881523a58919 | a067703ca430cb0a571ac31c9b90d21af8d0543b | It would be nice to have the xlabel as well. But not a must have. | kailashbuki | 144 |
py-why/dowhy | 764 | Add convenience function to plot attribution scores | We're using function in so many places in notebooks, similar to the graph plot utility that I think it's worth providing this as a convenience function. | null | 2022-11-15 22:35:03+00:00 | 2022-11-16 14:11:16+00:00 | dowhy/gcm/util/plotting.py | import logging
from typing import Any, Dict, List, Optional, Tuple
import networkx as nx
import pandas as pd
from matplotlib import pyplot
from networkx.drawing import nx_pydot
_logger = logging.getLogger(__name__)
def plot(
causal_graph: nx.Graph,
causal_strengths: Optional[Dict[Tuple[Any, Any], float]] = ... | import logging
from typing import Any, Dict, List, Optional, Tuple
import networkx as nx
import numpy as np
import pandas as pd
from matplotlib import pyplot
from networkx.drawing import nx_pydot
_logger = logging.getLogger(__name__)
def plot(
causal_graph: nx.Graph,
causal_strengths: Optional[Dict[Tuple[An... | petergtz | 7568d9528b7108830821e8cc729c881523a58919 | a067703ca430cb0a571ac31c9b90d21af8d0543b | Optionally: Can add something like `**kwargs_bar_plot`, which are passed into the `pyplot.bar` call. | bloebp | 145 |
py-why/dowhy | 764 | Add convenience function to plot attribution scores | We're using function in so many places in notebooks, similar to the graph plot utility that I think it's worth providing this as a convenience function. | null | 2022-11-15 22:35:03+00:00 | 2022-11-16 14:11:16+00:00 | dowhy/gcm/util/plotting.py | import logging
from typing import Any, Dict, List, Optional, Tuple
import networkx as nx
import pandas as pd
from matplotlib import pyplot
from networkx.drawing import nx_pydot
_logger = logging.getLogger(__name__)
def plot(
causal_graph: nx.Graph,
causal_strengths: Optional[Dict[Tuple[Any, Any], float]] = ... | import logging
from typing import Any, Dict, List, Optional, Tuple
import networkx as nx
import numpy as np
import pandas as pd
from matplotlib import pyplot
from networkx.drawing import nx_pydot
_logger = logging.getLogger(__name__)
def plot(
causal_graph: nx.Graph,
causal_strengths: Optional[Dict[Tuple[An... | petergtz | 7568d9528b7108830821e8cc729c881523a58919 | a067703ca430cb0a571ac31c9b90d21af8d0543b | I'll add them in a follow-up PR. | petergtz | 146 |
py-why/dowhy | 764 | Add convenience function to plot attribution scores | We're using function in so many places in notebooks, similar to the graph plot utility that I think it's worth providing this as a convenience function. | null | 2022-11-15 22:35:03+00:00 | 2022-11-16 14:11:16+00:00 | dowhy/gcm/util/plotting.py | import logging
from typing import Any, Dict, List, Optional, Tuple
import networkx as nx
import pandas as pd
from matplotlib import pyplot
from networkx.drawing import nx_pydot
_logger = logging.getLogger(__name__)
def plot(
causal_graph: nx.Graph,
causal_strengths: Optional[Dict[Tuple[Any, Any], float]] = ... | import logging
from typing import Any, Dict, List, Optional, Tuple
import networkx as nx
import numpy as np
import pandas as pd
from matplotlib import pyplot
from networkx.drawing import nx_pydot
_logger = logging.getLogger(__name__)
def plot(
causal_graph: nx.Graph,
causal_strengths: Optional[Dict[Tuple[An... | petergtz | 7568d9528b7108830821e8cc729c881523a58919 | a067703ca430cb0a571ac31c9b90d21af8d0543b | Fair. I'd still post-pone this into a separate PR. | petergtz | 147 |
py-why/dowhy | 762 | Add APIs for attributing unit change without a mechanism change | - Added two new APIs for attributing unit-level output changes when the mechanism does not change.
Signed-off-by: Kailash <111277+kailashbuki@users.noreply.github.com> | null | 2022-11-14 09:40:52+00:00 | 2022-11-14 21:51:34+00:00 | dowhy/gcm/unit_change.py | """This module provides the APIs for attributing the change in the output value of a deterministic mechanism for a statistical unit.
"""
from abc import abstractmethod
from typing import List, Optional
import numpy as np
import pandas as pd
from sklearn.linear_model._base import LinearModel
from sklearn.utils.validat... | """This module provides the APIs for attributing the change in the output value of a deterministic mechanism for a statistical unit.
"""
from abc import abstractmethod
from typing import List, Optional
import numpy as np
import pandas as pd
from sklearn.linear_model._base import LinearModel
from sklearn.utils.validat... | kailashbuki | 675cbc444cc422b007ba72fdf3303b328b123c48 | ac047066326d0de3646f8461c0c5c4dd9826287c | What about using ```if isinstance(mechanism, LinearPredictionModel)``` and then have only one method ```unit_change_input_only```? Same applies to the existing method.
To further simplify it (i.e., avoid having multiple different methods), you could also consider adding a parameter ```analyze_inputs_only``` to the o... | bloebp | 148 |
py-why/dowhy | 762 | Add APIs for attributing unit change without a mechanism change | - Added two new APIs for attributing unit-level output changes when the mechanism does not change.
Signed-off-by: Kailash <111277+kailashbuki@users.noreply.github.com> | null | 2022-11-14 09:40:52+00:00 | 2022-11-14 21:51:34+00:00 | dowhy/gcm/unit_change.py | """This module provides the APIs for attributing the change in the output value of a deterministic mechanism for a statistical unit.
"""
from abc import abstractmethod
from typing import List, Optional
import numpy as np
import pandas as pd
from sklearn.linear_model._base import LinearModel
from sklearn.utils.validat... | """This module provides the APIs for attributing the change in the output value of a deterministic mechanism for a statistical unit.
"""
from abc import abstractmethod
from typing import List, Optional
import numpy as np
import pandas as pd
from sklearn.linear_model._base import LinearModel
from sklearn.utils.validat... | kailashbuki | 675cbc444cc422b007ba72fdf3303b328b123c48 | ac047066326d0de3646f8461c0c5c4dd9826287c | Under a single method, a user still has to select an appropriate wrapper (`SklearnLinearRegressionModel` or `SklearnRegressionModel`) for sklearn models, and pass its object. At this point, just calling a more explicit API is better, no? It is also more transparent. A bit more theoretical argument: With `isinstance` pa... | kailashbuki | 149 |
py-why/dowhy | 762 | Add APIs for attributing unit change without a mechanism change | - Added two new APIs for attributing unit-level output changes when the mechanism does not change.
Signed-off-by: Kailash <111277+kailashbuki@users.noreply.github.com> | null | 2022-11-14 09:40:52+00:00 | 2022-11-14 21:51:34+00:00 | dowhy/gcm/unit_change.py | """This module provides the APIs for attributing the change in the output value of a deterministic mechanism for a statistical unit.
"""
from abc import abstractmethod
from typing import List, Optional
import numpy as np
import pandas as pd
from sklearn.linear_model._base import LinearModel
from sklearn.utils.validat... | """This module provides the APIs for attributing the change in the output value of a deterministic mechanism for a statistical unit.
"""
from abc import abstractmethod
from typing import List, Optional
import numpy as np
import pandas as pd
from sklearn.linear_model._base import LinearModel
from sklearn.utils.validat... | kailashbuki | 675cbc444cc422b007ba72fdf3303b328b123c48 | ac047066326d0de3646f8461c0c5c4dd9826287c | > Under a single method, a user still has to select an appropriate wrapper (SklearnLinearRegressionModel or SklearnRegressionModel) for sklearn models, and pass its object
That's right, one need to check if the model ```has_coefficients``` actually, since this is what you need in the linear solution. I see the conce... | bloebp | 150 |
py-why/dowhy | 762 | Add APIs for attributing unit change without a mechanism change | - Added two new APIs for attributing unit-level output changes when the mechanism does not change.
Signed-off-by: Kailash <111277+kailashbuki@users.noreply.github.com> | null | 2022-11-14 09:40:52+00:00 | 2022-11-14 21:51:34+00:00 | dowhy/gcm/unit_change.py | """This module provides the APIs for attributing the change in the output value of a deterministic mechanism for a statistical unit.
"""
from abc import abstractmethod
from typing import List, Optional
import numpy as np
import pandas as pd
from sklearn.linear_model._base import LinearModel
from sklearn.utils.validat... | """This module provides the APIs for attributing the change in the output value of a deterministic mechanism for a statistical unit.
"""
from abc import abstractmethod
from typing import List, Optional
import numpy as np
import pandas as pd
from sklearn.linear_model._base import LinearModel
from sklearn.utils.validat... | kailashbuki | 675cbc444cc422b007ba72fdf3303b328b123c48 | ac047066326d0de3646f8461c0c5c4dd9826287c | Force pushed a single helper API for all those four APIs. | kailashbuki | 151 |
py-why/dowhy | 762 | Add APIs for attributing unit change without a mechanism change | - Added two new APIs for attributing unit-level output changes when the mechanism does not change.
Signed-off-by: Kailash <111277+kailashbuki@users.noreply.github.com> | null | 2022-11-14 09:40:52+00:00 | 2022-11-14 21:51:34+00:00 | tests/gcm/test_unit_change.py | import numpy as np
import pandas as pd
import pytest
from flaky import flaky
from sklearn.ensemble import RandomForestRegressor as RFR
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import LinearRegression
from dowhy.gcm.ml.regression import SklearnRegressionModel
from dowhy.gcm.unit_change im... | import numpy as np
import pandas as pd
import pytest
from flaky import flaky
from sklearn.ensemble import RandomForestRegressor as RFR
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import LinearRegression
from dowhy.gcm.ml.regression import SklearnRegressionModel
from dowhy.gcm.unit_change im... | kailashbuki | 675cbc444cc422b007ba72fdf3303b328b123c48 | ac047066326d0de3646f8461c0c5c4dd9826287c | It looks like you don't really need `to_numpy()` as `assert_array_almost_equal` does this for you. | petergtz | 152 |
py-why/dowhy | 762 | Add APIs for attributing unit change without a mechanism change | - Added two new APIs for attributing unit-level output changes when the mechanism does not change.
Signed-off-by: Kailash <111277+kailashbuki@users.noreply.github.com> | null | 2022-11-14 09:40:52+00:00 | 2022-11-14 21:51:34+00:00 | tests/gcm/test_unit_change.py | import numpy as np
import pandas as pd
import pytest
from flaky import flaky
from sklearn.ensemble import RandomForestRegressor as RFR
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import LinearRegression
from dowhy.gcm.ml.regression import SklearnRegressionModel
from dowhy.gcm.unit_change im... | import numpy as np
import pandas as pd
import pytest
from flaky import flaky
from sklearn.ensemble import RandomForestRegressor as RFR
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import LinearRegression
from dowhy.gcm.ml.regression import SklearnRegressionModel
from dowhy.gcm.unit_change im... | kailashbuki | 675cbc444cc422b007ba72fdf3303b328b123c48 | ac047066326d0de3646f8461c0c5c4dd9826287c | Something I know now. Thank you. Force pushing the fix. | kailashbuki | 153 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring Causes of Hotel Booking Cancellations"
]
},
{
"attachments": {
"Screenshot%20from%202020-09-29%2019-08-50.png": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABcUAAAMoCAYAAAAOXYhzAAAAinpUWHRSYXcgcHJvZmlsZSB0eXBl... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring Causes of Hotel Booking Cancellations"
]
},
{
"attachments": {
"Screenshot%20from%202020-09-29%2019-08-50.png": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABcUAAAMoCAYAAAAOXYhzAAAAinpUWHRSYXcgcHJvZmlsZSB0eXBl... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | Good thought on rewording the title here. To keep the focus on the application, how about "Exploring Causes of Hotel Booking Cancellations" | amit-sharma | 154 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring Causes of Hotel Booking Cancellations"
]
},
{
"attachments": {
"Screenshot%20from%202020-09-29%2019-08-50.png": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABcUAAAMoCAYAAAAOXYhzAAAAinpUWHRSYXcgcHJvZmlsZSB0eXBl... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring Causes of Hotel Booking Cancellations"
]
},
{
"attachments": {
"Screenshot%20from%202020-09-29%2019-08-50.png": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABcUAAAMoCAYAAAAOXYhzAAAAinpUWHRSYXcgcHJvZmlsZSB0eXBl... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | Done | petergtz | 155 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/example_notebooks/dowhy_simple_example.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic Example for Calculating the Causal Effect\n",
"This is a quick introduction to the DoWhy causal inference library.\n",
"We will load in a sample dataset and estimate the causal effect of a (pre-specified) treatment vari... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic Example for Calculating the Causal Effect\n",
"This is a quick introduction to the DoWhy causal inference library.\n",
"We will load in a sample dataset and estimate the causal effect of a (pre-specified) treatment vari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | we could just say, "Causal Effect". | amit-sharma | 156 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/example_notebooks/dowhy_simple_example.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic Example for Calculating the Causal Effect\n",
"This is a quick introduction to the DoWhy causal inference library.\n",
"We will load in a sample dataset and estimate the causal effect of a (pre-specified) treatment vari... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic Example for Calculating the Causal Effect\n",
"This is a quick introduction to the DoWhy causal inference library.\n",
"We will load in a sample dataset and estimate the causal effect of a (pre-specified) treatment vari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | Done | petergtz | 157 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/example_notebooks/nb_index.rst | Example notebooks
=================
These examples are also available on `GitHub <https://github
.com/py-why/dowhy/tree/main/docs/source/example_notebooks>`_. You can `run them locally <https://docs.jupyter
.org/en/latest/running.html>`_ after cloning `DoWhy <https://github.com/py-why/dowhy>`_ and `installing Jupyter
... | Example notebooks
=================
These examples are also available on `GitHub <https://github
.com/py-why/dowhy/tree/main/docs/source/example_notebooks>`_. You can `run them locally <https://docs.jupyter
.org/en/latest/running.html>`_ after cloning `DoWhy <https://github.com/py-why/dowhy>`_ and `installing Jupyter
... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | nice thought to add the level. for the second bullet (API), given that the APIs will likely merge in the future, how about calling it a "Task"? We can think of effect estimation and Intervention via GCM as the two tasks here.
Edit: seeing the later examples, "Root Cause Analysis" may also fit nicely as a "Task" tha... | amit-sharma | 158 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/example_notebooks/nb_index.rst | Example notebooks
=================
These examples are also available on `GitHub <https://github
.com/py-why/dowhy/tree/main/docs/source/example_notebooks>`_. You can `run them locally <https://docs.jupyter
.org/en/latest/running.html>`_ after cloning `DoWhy <https://github.com/py-why/dowhy>`_ and `installing Jupyter
... | Example notebooks
=================
These examples are also available on `GitHub <https://github
.com/py-why/dowhy/tree/main/docs/source/example_notebooks>`_. You can `run them locally <https://docs.jupyter
.org/en/latest/running.html>`_ after cloning `DoWhy <https://github.com/py-why/dowhy>`_ and `installing Jupyter
... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | this one is "advanced" | amit-sharma | 159 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/example_notebooks/nb_index.rst | Example notebooks
=================
These examples are also available on `GitHub <https://github
.com/py-why/dowhy/tree/main/docs/source/example_notebooks>`_. You can `run them locally <https://docs.jupyter
.org/en/latest/running.html>`_ after cloning `DoWhy <https://github.com/py-why/dowhy>`_ and `installing Jupyter
... | Example notebooks
=================
These examples are also available on `GitHub <https://github
.com/py-why/dowhy/tree/main/docs/source/example_notebooks>`_. You can `run them locally <https://docs.jupyter
.org/en/latest/running.html>`_ after cloning `DoWhy <https://github.com/py-why/dowhy>`_ and `installing Jupyter
... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | the conditional effects notebook (dowhy-conditional-treatment-effects) is a key one that introduces users to compute the conditional causal effect. Given that many people use doWhy to estimate the conditional effect, one option is to include it in the "introductory examples" section. We can mark it as Level: Beginner ... | amit-sharma | 160 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/example_notebooks/nb_index.rst | Example notebooks
=================
These examples are also available on `GitHub <https://github
.com/py-why/dowhy/tree/main/docs/source/example_notebooks>`_. You can `run them locally <https://docs.jupyter
.org/en/latest/running.html>`_ after cloning `DoWhy <https://github.com/py-why/dowhy>`_ and `installing Jupyter
... | Example notebooks
=================
These examples are also available on `GitHub <https://github
.com/py-why/dowhy/tree/main/docs/source/example_notebooks>`_. You can `run them locally <https://docs.jupyter
.org/en/latest/running.html>`_ after cloning `DoWhy <https://github.com/py-why/dowhy>`_ and `installing Jupyter
... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | You're right. Originally, my thought was that there is the DoSampler API, GCM, and DoWhy's original "standard API", but we should make this forward-looking and we really want to focus on the tasks that users want to accomplish. 👍 | petergtz | 161 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/example_notebooks/nb_index.rst | Example notebooks
=================
These examples are also available on `GitHub <https://github
.com/py-why/dowhy/tree/main/docs/source/example_notebooks>`_. You can `run them locally <https://docs.jupyter
.org/en/latest/running.html>`_ after cloning `DoWhy <https://github.com/py-why/dowhy>`_ and `installing Jupyter
... | Example notebooks
=================
These examples are also available on `GitHub <https://github
.com/py-why/dowhy/tree/main/docs/source/example_notebooks>`_. You can `run them locally <https://docs.jupyter
.org/en/latest/running.html>`_ after cloning `DoWhy <https://github.com/py-why/dowhy>`_ and `installing Jupyter
... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | Added | petergtz | 162 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | cool title 👍 | amit-sharma | 163 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | typo: model. | amit-sharma | 164 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | we also use causal graphs for effect identification step. Since the two frameworks are mentioned above, I guess we can focus on an end-user's perspective here.
Maybe rephrase it to, "For effect inference, DoWhy offers a 4-step recipe consisting of modeling a causal graph, identification, estimation, and refutation. ... | amit-sharma | 165 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | haha, this might be too aggressive? How about,
To understand what these four steps mean (and why we need four steps), the best place to learn more is the user's guide ... chapter. Alternatively, you can dive into the code and explore basic features in the doc notebook." | amit-sharma | 166 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | given the new structure, should the further resources go into the user guide section where we talk about causality? | amit-sharma | 167 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | at this point, it will also be good to highlight the fact that we interface with EconML for conditional effects, since people have found that integration useful. We can add a sentence like,
"For estimation of conditional effects, you can also use methods from EconML using the same API, refer to this [notebook](dowhy-... | amit-sharma | 168 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | This actually not a new formulation, but an existing one:
- https://www.pywhy.org/dowhy/v0.8/user_guide/causality_intro.html?highlight=estimation
- https://www.pywhy.org/dowhy/main/getting_started/index.html#next-steps
I do get your point though. Alternative suggestion: Kailash and I have already started working r... | petergtz | 169 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | Yes, maybe. Have also already thought about the best place for this section. If it's okay, I would also like to post-pone this change until we have reworked the User Guide. Hopefully, that will give us a better understanding where this will fit. | petergtz | 170 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | Don e | petergtz | 171 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | Sounds good. We can postpone this. | amit-sharma | 172 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/getting_started/index.rst | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | Getting Started
===============
Installation
^^^^^^^^^^^^
The simplest installation is through `pip <https://pypi.org/project/dowhy/>`__ or conda:
.. tab-set-code::
.. code-block:: pip
pip install dowhy
.. code-block:: conda
conda install -c conda-forge dowhy
Further installation scenari... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | Sounds good! | amit-sharma | 173 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/index.rst | DoWhy documentation
===================
.. toctree::
:maxdepth: 3
:hidden:
:glob:
getting_started/index
User Guide <user_guide/index>
Examples <example_notebooks/nb_index>
dowhy
Contributing <contributing>
code_repo
**Date**: |today| **Version**: |version|
**Related resources**:... | DoWhy documentation
===================
.. toctree::
:maxdepth: 3
:hidden:
:glob:
getting_started/index
User Guide <user_guide/index>
Examples <example_notebooks/nb_index>
dowhy
Contributing <contributing>
code_repo
**Date**: |today| **Version**: |version|
**Related resources**:... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | love this new sentence +1 | amit-sharma | 174 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/index.rst | DoWhy documentation
===================
.. toctree::
:maxdepth: 3
:hidden:
:glob:
getting_started/index
User Guide <user_guide/index>
Examples <example_notebooks/nb_index>
dowhy
Contributing <contributing>
code_repo
**Date**: |today| **Version**: |version|
**Related resources**:... | DoWhy documentation
===================
.. toctree::
:maxdepth: 3
:hidden:
:glob:
getting_started/index
User Guide <user_guide/index>
Examples <example_notebooks/nb_index>
dowhy
Contributing <contributing>
code_repo
**Date**: |today| **Version**: |version|
**Related resources**:... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | Some of these are action links (check the source, raise an issue), so it may be worth putting them first.
Source Repo | Issues and Ideas | Join the community (Discord) | PyWhy organization | DoWhy on PyPI
| amit-sharma | 175 |
py-why/dowhy | 760 | Restructure documentation | The result of this can be seen at https://petergtz.github.io/dowhy/main
- Add logo to docs and README
- Move 4-step process documentation from docs landing page to effect inference user guide
- Provide documentation overview on docs starting page.
- Move citation page from docs landing page to user guide
- Intro... | null | 2022-11-11 14:23:31+00:00 | 2022-11-17 13:13:21+00:00 | docs/source/index.rst | DoWhy documentation
===================
.. toctree::
:maxdepth: 3
:hidden:
:glob:
getting_started/index
User Guide <user_guide/index>
Examples <example_notebooks/nb_index>
dowhy
Contributing <contributing>
code_repo
**Date**: |today| **Version**: |version|
**Related resources**:... | DoWhy documentation
===================
.. toctree::
:maxdepth: 3
:hidden:
:glob:
getting_started/index
User Guide <user_guide/index>
Examples <example_notebooks/nb_index>
dowhy
Contributing <contributing>
code_repo
**Date**: |today| **Version**: |version|
**Related resources**:... | petergtz | 30c102e358258b103fec8cd3ce42de9cf2051a50 | de8969a5c100e5186f7892f16073129b6d0cffc6 | Done | petergtz | 176 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conditional Average Treatment Effects (CATE) with DoWhy and EconML\n",
"\n",
"This is an experimental feature where we use [EconML](https://github.com/microsoft/econml) methods from DoWhy. Using EconML allows CATE estimation ... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conditional Average Treatment Effects (CATE) with DoWhy and EconML\n",
"\n",
"This is an experimental feature where we use [EconML](https://github.com/microsoft/econml) methods from DoWhy. Using EconML allows CATE estimation ... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | obtaining a build error. can you change the import of deepiv too? "from econml.iv.nnet import DeepIV"
CellExecutionError in example_notebooks/dowhy-conditional-treatment-effects.ipynb:
[147](https://github.com/py-why/dowhy/actions/runs/3568922214/jobs/5998324083#step:6:148)------------------
[148](https://gith... | amit-sharma | 177 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conditional Average Treatment Effects (CATE) with DoWhy and EconML\n",
"\n",
"This is an experimental feature where we use [EconML](https://github.com/microsoft/econml) methods from DoWhy. Using EconML allows CATE estimation ... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conditional Average Treatment Effects (CATE) with DoWhy and EconML\n",
"\n",
"This is an experimental feature where we use [EconML](https://github.com/microsoft/econml) methods from DoWhy. Using EconML allows CATE estimation ... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | Changed import. | andresmor-ms | 178 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conditional Average Treatment Effects (CATE) with DoWhy and EconML\n",
"\n",
"This is an experimental feature where we use [EconML](https://github.com/microsoft/econml) methods from DoWhy. Using EconML allows CATE estimation ... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conditional Average Treatment Effects (CATE) with DoWhy and EconML\n",
"\n",
"This is an experimental feature where we use [EconML](https://github.com/microsoft/econml) methods from DoWhy. Using EconML allows CATE estimation ... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | This is not needed, only need to specify the correct method_name parameter | andresmor-ms | 179 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | can you also add an example where estimate_effect is called again for a different (test) dataset? | amit-sharma | 180 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | You mean to call `estimate_effect()` without fitting and providing a new data? I'm not sure how that would work, would it be replacing the self._data without the other changes in the `fit()`?
Let me know how you expect it to work to be sure I implement it the correct way. | andresmor-ms | 181 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | docs/source/example_notebooks/dowhy_functional_api.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functional API Preview\n",
"\n",
"This notebook is part of a set of notebooks that provides a preview of the proposed functional API for dowhy. For details on the new API for DoWhy, check out https://github.com/py-why/dowhy/w... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | no, I meant that you fit an estimator once, and then call estimate_effect twice for two different datasets.
Just to show that we can fit once, and then call estimate_effect multiple times. | amit-sharma | 182 |
py-why/dowhy | 746 | Functional api/causal estimators | * Introduce `fit()` method to estimators.
* Refactor constructors to avoid using `*args` and `**kwargs` and have more explicit parameters.
* Refactor refuters and other parts of the code to use `fit()` and modify arguments to `estimate_effect()` | null | 2022-11-04 16:15:39+00:00 | 2022-12-03 17:07:53+00:00 | docs/source/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "0bbaacaa",
"metadata": {},
"source": [
"# Sensitivity analysis for non-parametric causal estimators\n",
"Sensitivity analysis helps us study how robust an estimated effect is when the assumption of no unobserved confounding is violated. That is, how ... | {
"cells": [
{
"cell_type": "markdown",
"id": "0bbaacaa",
"metadata": {},
"source": [
"# Sensitivity analysis for non-parametric causal estimators\n",
"Sensitivity analysis helps us study how robust an estimated effect is when the assumption of no unobserved confounding is violated. That is, how ... | andresmor-ms | 11c4e0dafd6e824eb81ad14262457d954ae61468 | affe0952f4aba6845247355c171565510c2c1673 | if there's an easy to remove the vscode hash from every notebook commit, that will be nice. I suspect this hash will change with every commit.
If not, it is fairly minor and we can ignore. | amit-sharma | 183 |
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