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causal inference | Causal Inference: Ignorability and Collider | https://stats.stackexchange.com/questions/541853/causal-inference-ignorability-and-collider | <p>I've encountered lots of causal inference terms and jargons (under the Neyman-Rubin potential outcome framework), and I had questions regarding ignorability.</p>
<p>Is it the case that <strong>ignorability</strong> is always a no <strong>sample selection bias</strong> condition?</p>
<p>And is this term equivalent to... | 700 | |
causal inference | Causal Inference: Moderation and Mediation | https://stats.stackexchange.com/questions/541852/causal-inference-moderation-and-mediation | <p>I've encountered lots of causal inference terms and jargons (under the Neyman-Rubin potential outcome framework), and I had a question regarding mediator and moderator.</p>
<p>Is it the case that <strong>moderation</strong> / moderators (interaction terms) necessarily implies unobserved causal <strong>mediation</str... | <p>Mediation and moderation are two unrelated concepts that can but do not always occur together.</p>
<p>Mediation occurs when the effect of one variable on another passes through a third variable, e.g., <span class="math-container">$A \rightarrow M \rightarrow Y$</span>. <span class="math-container">$M$</span> is a me... | 701 |
causal inference | Statistics and causal inference? | https://stats.stackexchange.com/questions/2245/statistics-and-causal-inference | <p>In his 1984 paper <a href="http://www-unix.oit.umass.edu/~stanek/pdffiles/causal-holland.pdf">"Statistics and Causal Inference"</a>, Paul Holland raised one of the most fundamental questions in statistics:</p>
<blockquote>
<p>What can a statistical model say about
causation?</p>
</blockquote>
<p>This led to hi... | <p>This is a broad question, but given the Box, Hunter and Hunter quote is true I think what it comes down to is</p>
<ol>
<li><p>The quality of the experimental design:</p>
<ul>
<li>randomization, sample sizes, control of confounders,...</li>
</ul></li>
<li><p>The quality of the implementation of the design:</p>
<ul... | 702 |
causal inference | What distinction is there between statistical inference and causal inference? | https://stats.stackexchange.com/questions/233235/what-distinction-is-there-between-statistical-inference-and-causal-inference | <p>Be it on a practical or theoretical level, what would you say are the key differences between statistical inference and causal inference.</p>
<p>I've been trying to learn more about causal inference and don't see a key difference in most instances.</p>
<p>If anything, I'd say that statistical inference is about fi... | <p>Causal inference is the process of <em>ascribing causal relationships</em> to associations between variables. Statistical inference is the process of using statistical methods to <em>characterize the association</em> between variables. Causality is at the root of scientific explanation which is considered to be ca... | 703 |
causal inference | Causal Inference: Selection Bias and Endogeneity | https://stats.stackexchange.com/questions/541807/causal-inference-selection-bias-and-endogeneity | <p>I've encountered lots of causal inference terms and jargons (under the Neyman-Rubin potential outcome framework), and I had questions regarding their relationships:</p>
<p>I know that exogeneity E(e|X) = 0 is a regression assumption that can be violated by omitted variable bias, and that selection bias is an omitted... | 704 | |
causal inference | Causal inference for additive multiple treatments | https://stats.stackexchange.com/questions/432298/causal-inference-for-additive-multiple-treatments | <p>I encountered a causal inference problem in practice and want to find if there is a previously established statistical toolset that can be applied to my problem.</p>
<p>My problem is characterized as follows:</p>
<ul>
<li>My goal is to characterize the causal effects of each <span class="math-container">$T$</span>... | <p>A1: A term sometimes used is "joint interventions". However, joint interventions explicitly refers to multiple treatments, <em>not</em> multiple treatments with the additivity assumption. <a href="https://www.who.int/publications/cra/chapters/volume2/2191-2230.pdf" rel="nofollow noreferrer">This chapter</a> may be a... | 705 |
causal inference | Causal inference and Propensity score | https://stats.stackexchange.com/questions/642327/causal-inference-and-propensity-score | <p>I am trying to understand Rubin's causal model but I can not make the connection between certain notions.
The problem of causal inference lies in calculating the counterfactual, i.e. knowing what the outcome would have been in the absence/with treatment.</p>
<p>The causal effect is individual (and unobservable), so ... | <p>Strictly speaking propensity score (PS) analysis is not a causal method. It is just a “confounder concentrator” or data reduction method. It allows you to use fewer parameters in the outcome model and still capture confounding. You still must adjust for outcome heterogeneity in addition to differences in baseline... | 706 |
causal inference | Causal inference with (only) interval uncertainty? | https://stats.stackexchange.com/questions/626310/causal-inference-with-only-interval-uncertainty | <p>The modern popular frameworks that I am aware of for causal inference (i.e. potential outcomes or Pearlian) are based on a premise of uncertainty quantified as probability. There's nothing particularly wrong with that, but I like to explore tools and use cases.</p>
<p><a href="https://en.wikipedia.org/wiki/Interval_... | <p>I'm not aware of anything using Interval Arithmetic, but perhaps the idea behind <a href="https://arxiv.org/abs/1501.01332" rel="nofollow noreferrer">invariant causal prediction</a> comes close. The way it's described and implemented in the article it's still very much rooted in probabilities, but the fundamental id... | 707 |
causal inference | What is the relation between causal inference and prediction? | https://stats.stackexchange.com/questions/56909/what-is-the-relation-between-causal-inference-and-prediction | <p>What are the relationships and the differences between causal inference and prediction (both classification and regression)?</p>
<p>In the prediction context, we have the predictor/input variables and response/output variables. Does that mean that there is causal relation between input and output variables? So, doe... | <p>Causal inference is focused on knowing what happens to <span class="math-container">$Y$</span> when you change <span class="math-container">$X$</span>. Prediction is focused on knowing the next <span class="math-container">$Y$</span> given <span class="math-container">$X$</span> (and whatever else you've got). </p>... | 708 |
causal inference | Regression in Causal Inference | https://stats.stackexchange.com/questions/479432/regression-in-causal-inference | <p>I was recently introduced to the topic of causal inference in statistics and I am currently learning about the importance of the backdoor criterion (BDC), as applied to the following DAG. Interest lies in assessing the causal effect of the treatment <span class="math-container">$X$</span> upon the outcome <span clas... | <p>Just to add to the excellent answers by Adrian and Noah, there is the residual question of:</p>
<blockquote>
<p>how to establish which of the three sets of variables given above should be conditioned on.</p>
</blockquote>
<p>Fist let's recap how the backdoor criterion is applied to this particular DAG, which I'm rep... | 709 |
causal inference | Causal inference from a cross sectional study design | https://stats.stackexchange.com/questions/147443/causal-inference-from-a-cross-sectional-study-design | <p>As far I know, causal inference can be made only from longitudinal study designs. Is there any way to make causal inference from a cross sectional study design? If yes, how can I do this? Please share if any literature is available. </p>
| <p>You could also use pcalg package if you are interested in network analysis(graphical modeling) and creating directed causal networks. pcalg has several algorithms for observational(cross sectional) data.
With assumption of no hidden variable, you could use "pc" algorithm in the package to estimate the equivalence c... | 710 |
causal inference | Machine learning for causal inference | https://stats.stackexchange.com/questions/565090/machine-learning-for-causal-inference | <p>I have a multiclass classification problem where the target variable is actually different categories of causes, and the dataset is observational. I know of causal inference, and I would like to learn more about it, but if I do I would need to justify it. So: is it justified to believe that a causal approach would y... | <p>Start with the <a href="https://stats.stackexchange.com/questions/6/the-two-cultures-statistics-vs-machine-learning">The Two Cultures: statistics vs. machine learning?</a> thread. Machine learning is about finding patterns or correlations in data. Causal inference, like statistics, is about inference. As others alre... | 711 |
causal inference | Bias-Variance tradeoff in prediction versus causal inference | https://stats.stackexchange.com/questions/620053/bias-variance-tradeoff-in-prediction-versus-causal-inference | <p>In prediction, accepting a little more bias in exchange for a lot less variance is the very name of the game - we'll chose the model with minimal test MSE without regard for its composition (bias squared versus variance). In causal inference, we rarely - if ever - are willing to make this tradeoff. The emphasis/weig... | <h4>Estimators should be judged as normal (so biased estimators are not ruled out), but with appropriate experimental protocols to deal with causality</h4>
<p>I disagree with some of the other answers here. When conducting causal analysis, there is still a distinction between attempting to make inferences about unknow... | 712 |
causal inference | Causal inference for continuous exposures | https://stats.stackexchange.com/questions/535388/causal-inference-for-continuous-exposures | <p>I am new to causal inference world and want to find which is the correct statistical procedure that can be applied to my data.
I found a number of predictors 𝑋<sup>1...n</sup> which are associated with a continuous outcome 𝑌 in a cross-sectional setting (N<sub>samples</sub>~1000), both the predictors and the outco... | 713 | |
causal inference | AIC for Causal Inference | https://stats.stackexchange.com/questions/398740/aic-for-causal-inference | <p>I read <a href="https://stats.stackexchange.com/questions/78295/using-aic-to-test-the-direction-of-causality">a post</a> explaining why the Akaike Criterion cannot be used for deciding if A cause B or B caused A.</p>
<p>I'm curious about a more general case of using AIC for causal inference (with observational data... | 714 | |
causal inference | Time length for causal inference experiments | https://stats.stackexchange.com/questions/518475/time-length-for-causal-inference-experiments | <p>Let's say that I want to run a causal inference experiment, that is an experiment on historical data for an intervention that we were not able to perform a randomized controlled trial for. In the case of something like a difference-in-differences (DD), or even just a basic linear/logit regression, for the purpose of... | <blockquote>
<p>In the case of something like a difference-in-differences (DD), or even just a basic linear/logit regression, ... is there a rule of thumb for attempting to control for the length of time to use in the pre-intervention period?</p>
</blockquote>
<p>In a difference-in-differences (DD) setting, we often wa... | 715 |
causal inference | Causal Inference in Mortality Rates | https://stats.stackexchange.com/questions/419136/causal-inference-in-mortality-rates | <p>I was wondering how does one study the average treatment affect in scenarios suchs as mortality rates.</p>
<p>For example: suppose we want to study the effect that a certain medicine has on the mortality rates os the patients. How can we do a study such as Difference-In-Differences or Propensity Scores if the diffe... | <p>Patients will likely differ in terms of measurable pre-treatment attributes. If you have access to these covariates, they should be an input to you adjusting method of choice (i.e. inverse propensity weighting). What do you mean by "differences before the treatment are zero?" If by that you mean that the propensity ... | 716 |
causal inference | Causal Inference: Meta Learners usage | https://stats.stackexchange.com/questions/645660/causal-inference-meta-learners-usage | <p>I have been running causal inference using Econ ML package on my data. I have a dataset containing customers divided into treatment and control and many other features. I run matching on those and obtained a matched dataset that contains the matched treat and control. If I calculate the difference in the avg outcome... | <p><strong>Disclaimer</strong> I just read the Econ ML package right now, very briefly. I also am not sure how "matching" comes into the model. Take my answer with a grain of salt, I may have misunderstood.</p>
<p><strong>TL;DR</strong> Your first estimate (with the "matching") is closer to the real... | 717 |
causal inference | Exchangeability, causal inference, and partial pooling | https://stats.stackexchange.com/questions/560761/exchangeability-causal-inference-and-partial-pooling | <p>In Statistical Rethinking, Richard McElreath writes the following concerning the use of partial pooling (i.e. varying/random effects) in Bayesian hierarchical models:</p>
<blockquote>
<p>Could we also use partial pooling on the treatment effects? Yes, we could. Some people will scream “No!” at this suggestion, becau... | 718 | |
causal inference | Where does multilevlel modeling fit in with causal inference? | https://stats.stackexchange.com/questions/617895/where-does-multilevlel-modeling-fit-in-with-causal-inference | <p>I am just now exploring the world of multilevel modeling and I am wondering how to contextualize MLM within the broader toolkit of causal inference techniques. In one of my graduate econometrics course, I was taught the fixed effects v. random effects dichotomy that <a href="https://theeffectbook.net/ch-FixedEffects... | <p>I think this question is conflating a few distinct issues.</p>
<p>First of all, the terms "multilevel modeling," "random effects," and "fixed effects" are all used in different ways by different people. <a href="https://stats.stackexchange.com/questions/4700/what-is-the-difference-betwe... | 719 |
causal inference | Variable type name in causal inference | https://stats.stackexchange.com/questions/546510/variable-type-name-in-causal-inference | <p>Causal inference language distinguishes different variable types: confounders, mediators, colliders, moderators.</p>
<p>Some time ago I encountered quite rare variable name which I can not remember. The idea of it was that only a part of the confounding variable caused outcome and variable of interest, while the oth... | <p>You are probably thinking of a component cause, part of the sufficient component causal model. It is described briefly <a href="https://sphweb.bumc.bu.edu/otlt/mph-modules/ep/ep713_causality/ep713_causality4.html" rel="nofollow noreferrer">here</a>.</p>
| 720 |
causal inference | Regression Methods in Causal Inference | https://stats.stackexchange.com/questions/601289/regression-methods-in-causal-inference | <p>In the most basic regression methods of causal inference (randomized experiment case), it's known that we can use covariates to predict the observed outcome, i.e. <span class="math-container">$Y^{obs}$</span> and the model is
<span class="math-container">$$
Y^{obs}_i=\alpha+\tau W_i+\beta X+\epsilon_i
$$</span>
in w... | <p>Yes, we can use a non-linear estimator to reduce the variance and get more accurate results. There are many different techniques. To start of, for example, BART (<a href="https://projecteuclid.org/journals/annals-of-applied-statistics/volume-4/issue-1/BART-Bayesian-additive-regression-trees/10.1214/09-AOAS285.full" ... | 721 |
causal inference | Causal inference on time-series data: is intervention needed? | https://stats.stackexchange.com/questions/631678/causal-inference-on-time-series-data-is-intervention-needed | <p>I'm working on the topic of causal inference, I use time-series data. I have two scenarios in front of me and I don't understand the difference:</p>
<ul>
<li>Given X and Y "time" features. I would like to know whether X, e.g. average income, does it cause Y, e.g. hotel reservations.</li>
<li>Given X "... | <p>Both are identical problems. The purpose of intervention is the measure causal effect size between X and Y. For example if income is increased at some point in time, if the same unit (person or family) would consume more hotel reservation services. Such that, in do-calculus notation, <span class="math-container">$P(... | 722 |
causal inference | Causal Inference Short Time Series | https://stats.stackexchange.com/questions/555626/causal-inference-short-time-series | <p>I am trying to analyse causal inference associated with an intervenion using either Difference-in-Differences or Interrupted Time Series Analysis. I have a discrete time series consisting of data covering a four year period, which could either be aggregated by month [allowing for 24 observations in both the pre- and... | 723 | |
causal inference | A Short Video to Explain Causal Inference to Non-Technical Audiences | https://stats.stackexchange.com/questions/638811/a-short-video-to-explain-causal-inference-to-non-technical-audiences | <p>I thought this question might be too much like a shopping question so I initially asked in <a href="https://chat.stackexchange.com/transcript/message/65133449#65133449">Ten Fold</a>, but it was <a href="https://chat.stackexchange.com/transcript/message/65135899#65135899">suggested that I ask here</a>. Here is my ori... | 724 | |
causal inference | Do-calculus and causal inference for continuous random variables | https://stats.stackexchange.com/questions/604373/do-calculus-and-causal-inference-for-continuous-random-variables | <p>Typical treatments of do-calculus and causal inference use discrete random variables. For example, the first rule of do-calculus in Pearl states:</p>
<p><a href="https://i.sstatic.net/GHK33.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/GHK33.png" alt="enter image description here" /></a></p>
<p>I'm ... | 725 | |
causal inference | Why isn't causal inference a simple specialized regression problem? | https://stats.stackexchange.com/questions/464470/why-isnt-causal-inference-a-simple-specialized-regression-problem | <p>I am often told that the crucial difficulty in causal inference is that we only observe one value between <span class="math-container">$Y(1)$</span> and <span class="math-container">$Y(0)$</span> while we want to estimate <span class="math-container">$E[Y(1) - Y(0)]$</span>. There is always an unobserved value.</p>
... | <p>A real-life example for how you run into problems: People with prior heart attacks take various drugs like beta blockers. The more severe the patient state, the more like it is that they are prescribed the drug. If you do not know all that much about patients and just take a bunch of patients with a heart attack in ... | 726 |
causal inference | Positivity assumption in causal inference with continuous covariates | https://stats.stackexchange.com/questions/582471/positivity-assumption-in-causal-inference-with-continuous-covariates | <p>In causal inference, studies usually require several assumptions (e.g., Unconfoundedness) to make valid causal statements. One of these assumptions is the 'Positivity' Assumption (sometimes referred to as 'Common Support' / 'Overlap'). With measured covariates L, this assumption can be defined as:</p>
<blockquote>
<... | 727 | |
causal inference | Is causal inference only from data possible? | https://stats.stackexchange.com/questions/384330/is-causal-inference-only-from-data-possible | <p>Suppose we are given a dataset but not the capability of performing some AB testing. We do some regression using X as predictor and Y as response and get a model. Can we actually say something about the causal relationship between X and Y? Or is it simply impossible to say anything about the causal relationship at a... | <blockquote>
<p>Suppose we are given a dataset but not the capability of performing
some AB testing. We do some regression using X as predictor and Y as
response and get a model. Can we actually say something about the
causal relationship between X and Y?</p>
</blockquote>
<p>No you can't, even when all varia... | 728 |
causal inference | Equation 3.6 Elements of Causal Inference | https://stats.stackexchange.com/questions/618819/equation-3-6-elements-of-causal-inference | <p>I am reading Elements of Causal Inference by Peters et al.</p>
<p>On page 36 they are giving an example with the following SCM:</p>
<p><span class="math-container">$$T := N_T$$</span>
<span class="math-container">$$B := T\cdot N_B + (1 - T)\cdot(1 - N_B)$$</span></p>
<p>On the equation 3.6, when talking about counte... | <p>Your second and third pair of equations are the same, so there is no contradiction as far as I can see.
The conditioning on <span class="math-container">$B, T$</span> is done to determine the value of <span class="math-container">$N_B$</span>, which is otherwise unknown, so there is no way to condition on it.
If <sp... | 729 |
causal inference | Textbook recommendations covering machine learning techniques for causal inference? | https://stats.stackexchange.com/questions/548929/textbook-recommendations-covering-machine-learning-techniques-for-causal-inferen | <p>Over the past 15 years there has been progress in adapting machine learning methods for causal inference. For example: targeted learning, double machine learning, causal trees.</p>
<p>Is there a textbook that covers the current range of techniques? I haven't seen anything on Amazon, perhaps there are texts available... | <p>I follow this area pretty closely, but I think this subfield is so new no textbook exists (yet).</p>
<p>However, there are some course videos that are fairly good:</p>
<ol>
<li><a href="https://youtube.com/playlist?list=PLxq_lXOUlvQAoWZEqhRqHNezS30lI49G-" rel="noreferrer">Machine Learning & Causal Inference: A S... | 730 |
causal inference | Why should we care about DAGs for causal inference? | https://stats.stackexchange.com/questions/565808/why-should-we-care-about-dags-for-causal-inference | <p>I am not acquainted with Pearl's approach for causal inference. From what I have seen so far, the causality is inferred from directed acyclic graphs(DAGs).</p>
<p>Rubin's Causal Inference Sec 7.5 proved a theorem stating that asymptotic unbiasedness of OLS estimator for superpopulation treatment effect.</p>
<p>By Ru... | 731 | |
causal inference | Should predictive analysis be tackled with causal inference in mind? | https://stats.stackexchange.com/questions/561704/should-predictive-analysis-be-tackled-with-causal-inference-in-mind | <p>Say I am trying to predict depression from anxiety. I collect data and build a MLE and obtain r=0.9. To me, this is great, so I push the model to production. 4 months later, I realise that the "rate of unemployment" is a confounder that plays on both variables.<br />
I conclude that I should not merely loo... | <p>As Vladimir mentions, the answer is "it depends". If you build a well-calibrated correlational (i.e., predictive) model on units randomly sampled from your population, then that model should generalize to other members of the population. If you have a model of depression and anxiety, then applying that mod... | 732 |
causal inference | Can cross validation be used for causal inference? | https://stats.stackexchange.com/questions/3893/can-cross-validation-be-used-for-causal-inference | <p>In all contexts I am familiar with cross-validation it is solely used with the goal of increasing predictive accuracy. Can the logic of cross validation be extended in estimating the unbiased relationships between variables? </p>
<p>While <a href="http://dx.doi.org/10.1007/s10940-009-9077-7" rel="noreferrer">this</... | <p>I think it's useful to review what we know about cross-validation. Statistical results around CV fall into two classes: efficiency and consistency.</p>
<p>Efficiency is what we're usually concerned with when building predictive models. The idea is that we use CV to determine a model with asymtptotic guarantees con... | 733 |
causal inference | Matching vs simple regression for causal inference? | https://stats.stackexchange.com/questions/431939/matching-vs-simple-regression-for-causal-inference | <p>This is a really simple, newbie question. I am really confused about the notion of matching and when it can be used instead of a multiple regression?</p>
<p>Assume I have listed all the confounding variables (X), and my outcome (Y) and treatment assignment (A) are binary.</p>
<p>Can I reach causal inference only b... | <p>Your question rightly acknowledges that throwing away cases can lose useful information and power. It doesn't, however, acknowledge the danger in using regression as the alternative: what if your regression model is incorrect?</p>
<p>Are you sure that the log-odds of outcome are linearly related to treatment and to... | 734 |
causal inference | Causal Inference for experiment | https://stats.stackexchange.com/questions/573032/causal-inference-for-experiment | <p>I'm working through a textbook (Regression and Other Stories) and have come across a particular problem that I am having difficulty convincing myself I understand.</p>
<p>I am specifically interested in part (b), but I include (a) as context.</p>
<p>It is as follows</p>
<p>'Before-after comparisons: The folder Sesam... | <p>The validity of the post-pre estimator in the intervention group depends on there being no change in the control group. If there was change in the control group, then any changes you see in the treatment group could be due either to the treatment or to whatever caused changes in the control group. For example, if co... | 735 |
causal inference | Online resources for philosophy of causation for causal inference | https://stats.stackexchange.com/questions/62025/online-resources-for-philosophy-of-causation-for-causal-inference | <p>Can you recommend any books, articles, essays, online tutorials/courses, etc that would be interesting and useful for an epidemiologist/biostatistician to learn about the philosophy of causation/causal inference?</p>
<p>I know quite a bit about actually doing causal inference from an epi and biostats framework, but... | <p>Without wanting to delve into specific papers, I think an excellent resource for something like that would be the <a href="http://plato.stanford.edu/" rel="nofollow noreferrer">Stanford Encyclopedia of Philosophy</a>. The lemmas on <a href="http://plato.stanford.edu/entries/causation-probabilistic/" rel="nofollow n... | 736 |
causal inference | What should I study after finishing 'Causal Inference in Statistics: A Primer'? | https://stats.stackexchange.com/questions/576913/what-should-i-study-after-finishing-causal-inference-in-statistics-a-primer | <p>I have almost finished studying 'Causal Inference in Statistics: A Primer', but I still feel that I need to learn more.<br />
I considered 'Causality' (Pearl, 2009), but there seem to be several good learning resources about DAG (ex. Review Paper & etc).<br />
What should I study after finishing 'Causal Inferenc... | 737 | |
causal inference | Why use causal inference if coefficients are same in an OLS? | https://stats.stackexchange.com/questions/617367/why-use-causal-inference-if-coefficients-are-same-in-an-ols | <p>I was reading this <a href="https://towardsdatascience.com/the-fwl-theorem-or-how-to-make-all-regressions-intuitive-59f801eb3299" rel="nofollow noreferrer">amazing article</a> about FWL theorem and it's application to causal inference.</p>
<p>In the article, there are some examples showing that the coefficients of a... | <h2>Causal inference is all about what to estimate, not about how to estimate it</h2>
<p>The point of causal inference is not to reduce multivariate regressions into univariate ones. The point of causal inference is to identify <strong>what</strong> estimand to estimate to begin with.
The article in question gives you ... | 738 |
causal inference | Correlation , Regression and Causal inference | https://stats.stackexchange.com/questions/260677/correlation-regression-and-causal-inference | <p>Based on several posts i read on stack exchange I now know that neither correlation nor regression indicate causation, </p>
<p>then why is it said that the 2 main uses of regression are
1)prediction
2)causal analysis and inference ??</p>
<p>Reference to the following article by Dr Paul Allison </p>
<p><a href="... | <blockquote>
<p>In a causal analysis, the independent variables are regarded as causes of the dependent variable. The aim of the study is to determine whether a particular independent variable really affects the dependent variable, and to estimate the magnitude of that effect, if any.”</p>
</blockquote>
<p>If your k... | 739 |
causal inference | Causal inference using regression for multiple covariates | https://stats.stackexchange.com/questions/458211/causal-inference-using-regression-for-multiple-covariates | <p>I am reading lot of material regarding Causal Inference using Regression Analysis but I am unable to resolve my doubt.</p>
<p>Suppose I have a data with Outcome <strong><em>Y</em></strong>, Treatment <strong><em>Tr</em></strong> and covariates <strong><em>X1, X2, X3, X4, ....</em></strong> </p>
<p>I need to find A... | <p>For the model 3 ATE will be the following</p>
<pre><code>ATE = beta0 + beta1*X1 + beta2*X2 + beta3*X3 + beta4*X4 + ...........
</code></pre>
| 740 |
causal inference | How do we select model for causal inference? | https://stats.stackexchange.com/questions/565783/how-do-we-select-model-for-causal-inference | <p>I am reading Rubin's Causal Inference Sec 7.5 in context of completely randomized experiment.</p>
<ol>
<li><p>It says performing linear regression will produce asymptotically unbiased estimate of causal effect, independent of whether model is misspecified.</p>
</li>
<li><p>However, in the later section, it says inco... | 741 | |
causal inference | Why do we need a consistency assumption in causal inference? | https://stats.stackexchange.com/questions/631817/why-do-we-need-a-consistency-assumption-in-causal-inference | <p>Why do we need a consistency assumption in causal inference? I think the consistency assumption is quite obvious and it is more like a definition for the observed outcome.</p>
<p><a href="https://i.sstatic.net/8nyuQ.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/8nyuQ.png" alt="enter image descriptio... | <p>In one <a href="https://www.nature.com/articles/ijo200882" rel="noreferrer">article</a>, Hernán indeed writes</p>
<blockquote>
<p>"Consistency may seem so obvious as to hardly deserve any attention. As a consequence, the condition of consistency is
often taken for granted [...]"</p>
</blockquote>
<p>Then, ... | 742 |
causal inference | In what cases is identification not possible in causal inference? | https://stats.stackexchange.com/questions/617240/in-what-cases-is-identification-not-possible-in-causal-inference | <p>In step one of judea pearls causal inference book it is to define your graphical causal model. The second step is identification of the estimand for estimation in step 3. Are there any cases where identification may not be possible? i.e. where our dowhy expression cannot be expressed in terms of conditional expectat... | <p>The causal effect of <span class="math-container">$X$</span> on <span class="math-container">$Y$</span> is not identifiable in a number of cases. Pearl's <em>Causality: Models, Reasoning, and Inference, 2nd Ed.</em> (2009), on p. 90, has three examples. The simplest possible such example is the graph consisting of t... | 743 |
causal inference | Causal inference - propensity score balancing sufficient for potential outcome balancing? | https://stats.stackexchange.com/questions/632249/causal-inference-propensity-score-balancing-sufficient-for-potential-outcome-b | <p>I am trying to make some causal inference estimates in a dataset and was hoping someone here could help me out with a question I have coming out of my background reading.</p>
<p>It seems that a very prevalent technique is to use a propensity score (as described by Rosenbaum, the probability of receiving the treatmen... | <p>You might be missing the key assumption required for propensity score methods (and all methods that rely on covariate adjustment, of which propensity score methods are a set, and likely not even among the best methods) to yield valid estimates of the causal effect: strong ignorability. String ignorability says that ... | 744 |
causal inference | Conditional expectation function and causal inference | https://stats.stackexchange.com/questions/637141/conditional-expectation-function-and-causal-inference | <p><strong>!For the question itself skip to the last paragraph!</strong></p>
<p>It is my understanding that iff we have a model of the form <span class="math-container">$$Y = m(X) + e$$</span> and <span class="math-container">$E[e|X] = 0$</span> we know that <span class="math-container">$m(X)$</span> is the conditional... | <h1>Linearity is a property of the function, not of the RVs</h1>
<p>Given the comments I think I'm beginning to see what the confusion is, so I'll attempt an answer.</p>
<p>The assumption of linearity does not rest upon the variable of intervention (treatment), the covariates, or the target variable being binary random... | 745 |
causal inference | Portfolio Optimization using causal inferences | https://stats.stackexchange.com/questions/605148/portfolio-optimization-using-causal-inferences | <p>I'm trying to use causal inferences in portfolio optimization and I used CausalImpact library in python because it deals with time series. I wanted to check the effect of covid19 on the daily closing prices, so I selected the prior and post period as the period before and after 2019-03-01 respectively.
Since there a... | <p>The index is combination of stocks that were all touched by the pandemic, as you admitted, so it cannot serve as a valid counterfactual.</p>
| 746 |
causal inference | Difference between exchangeability and independence in causal inference | https://stats.stackexchange.com/questions/558195/difference-between-exchangeability-and-independence-in-causal-inference | <p>When inferring causal effects from observational studies, one of the assumptions that's generally required is the exchangeability assumption. Suppose <span class="math-container">$A \in \{0, 1\}$</span> is a binary treatment, and let <span class="math-container">$Y^a$</span> denote the counterfactual outcome under t... | <blockquote>
<p>My question is, why is this assumption called the "exchangeability"
assumption when it's a statement about independence?</p>
</blockquote>
<p>Exchangeability is the assumption of being able to <strong>exchange</strong> groups without changing the outcome of the study. Why? Because the relation... | 747 |
causal inference | Non-obvious real-world datasets for observational causal inference | https://stats.stackexchange.com/questions/347899/non-obvious-real-world-datasets-for-observational-causal-inference | <p>I am working on a project involving inference of causal direction from purely observational data, and not time series (given several assumptions, of course). I've been using the <a href="https://webdav.tuebingen.mpg.de/cause-effect/" rel="nofollow noreferrer">CauseEffectPairs</a> database to validate my method, but ... | <p>I am not sure about what types of datasets you would be looking at, but I can suggest a measure.</p>
<p>Somers' D is an asymmetric measure of association. It distinguishes between it is raining, therefore, there must be clouds from there are clouds, therefore, it must be raining. It won't always work, however. P... | 748 |
causal inference | Pearl's Causal Inference In Statistics, equation 3.11 - Calculation of group specific causal effects | https://stats.stackexchange.com/questions/602716/pearls-causal-inference-in-statistics-equation-3-11-calculation-of-group-spe | <p>In the book <em>Causal Inference In Statistics</em> by <em>Pearl</em>, page 63, while referring to the below DAG, it says -</p>
<blockquote>
<p>Thus to compute the <span class="math-container">$w$</span>-specific causal effect, written
<span class="math-container">$P(y|do(x),w)$</span>, we adjust for <span class="ma... | <p>The <span class="math-container">$w$</span>-specific causal effect of <span class="math-container">$X$</span> on <span class="math-container">$Y$</span> is quite distinct from the causal effect of <span class="math-container">$X$</span> on <span class="math-container">$Y.$</span> The causal effect of <span class="ma... | 749 |
causal inference | Is there sense in applying causal inference methods to variables with low correlation? | https://stats.stackexchange.com/questions/187008/is-there-sense-in-applying-causal-inference-methods-to-variables-with-low-correl | <p>This question is somehow similar to <a href="https://stats.stackexchange.com/questions/26300/does-causation-imply-correlation">Does causation imply correlation?</a>, but what I would like to know is there any sense in applying a causal inference methods when we have a low correlation level. I'm very interested in a ... | <p>Statements on how "meaningfull" something is, necessarily involves a subjective assessment. So, it depends: A not very precise estimate for a very relevant topic for which there is no more precise measure yet, is probably very meaningful, as long as it is interpreted with the right degree of caution.</p>
<p>Moreove... | 750 |
causal inference | Bayesian Networks vs traditional stats approaches to Causal Inference? | https://stats.stackexchange.com/questions/554690/bayesian-networks-vs-traditional-stats-approaches-to-causal-inference | <p>I've been reading the 'book of why' by Judea Pearl and come to understand that Bayesian Networks can be used to establish causality given a directed acyclic graph (DAG) and that the methods are non-parametric. Throughout the book, the author drags Pearson and Fisher through the mud; it can be hard to tell what is an... | 751 | |
causal inference | Causal inference of impact of a university bankruptcy | https://stats.stackexchange.com/questions/656592/causal-inference-of-impact-of-a-university-bankruptcy | <p>I have taken one introductory course in causal inference but I'm very new to this.</p>
<p>I have one problem I'm thinking of tackling. There are 124 electoral districts in Ontario. ED boundaries were the same from 2018 to 2022, but there was a high-profile university bankruptcy in one remote district in 2021. I woul... | 752 | |
causal inference | Is the emmeans R package performing causal inference G-computation? | https://stats.stackexchange.com/questions/520389/is-the-emmeans-r-package-performing-causal-inference-g-computation | <p>So I am trying to get an understanding of causal inference and how it differs from the usual contrasts. I regularly use the emmeans package in R, and I am wondering when the function emmeans() mentions it has averaged over the covariates is this essentially performing G-computation? At least for regular OLS or ident... | <p>Re-reading your question, my understanding is that you are asking if <code>emmeans()</code> does G-computation as part of what it <em>ordinarily</em> does. And based on my very limited understanding of causal models and G-computation, I would say the answer is <em>NO</em>. That is simply because we don't treat covar... | 753 |
causal inference | Variable selection in causal inference regression models when $p > n$? | https://stats.stackexchange.com/questions/623100/variable-selection-in-causal-inference-regression-models-when-p-n | <p>Are there accepted techniques for selecting variables in causal inference (not prediction) where the number of variables exceeds our sample size, making a standard OLS regression impossible?</p>
<p>Assume treatment, outcome, and covariate variables have been carefully selected with a causal diagram, based on subject... | 754 | |
causal inference | What are key papers discussing causal inference from a missing data perspective? | https://stats.stackexchange.com/questions/222939/what-are-key-papers-discussing-causal-inference-from-a-missing-data-perspective | <p>The Rubin Causal Model (RCM), also called Potential Outcome Framework, assumes any unit in a population has potential outcomes under any treatment relevant in a study. For example $Y_1$ denotes the outcome under treatment, $Y_0$ the outcome under control. In a non-randomized experiment the fact that in expectation t... | <p>$$E(Y_1−Y_0)$$
is the quantity we would like to learn about. Counterfactuals per se are not observed, so we need to make further assumptions to write this counterfactual quantity in terms of the observed variables $Y$ and $T$. One way is to assume that
$$Y_1, Y_0 \perp T,$$
for example because treatment is randomi... | 755 |
causal inference | Causal inference in python - where to start? | https://stats.stackexchange.com/questions/545054/causal-inference-in-python-where-to-start | <p><strong>Point 1</strong>: I'm not sure if this question could be asked here, as it is may not seem to be about the "science" itself at the first glance! At the second glance though, I think in practice several newbies would face this question and it is a public benefit to have it for reference of people</p... | <p>Here are a few good websites/books that I am fond of that use DAGs, and have code examples in R, Python, and Stata on github or packaged up.</p>
<ul>
<li><p><a href="https://mixtape.scunning.com/index.html" rel="nofollow noreferrer">Causal Inference: The
Mixtape</a> and <a href="https://github.com/scunning1975/mixta... | 756 |
causal inference | When is it valid to use race/ethnicity in causal inference? | https://stats.stackexchange.com/questions/366301/when-is-it-valid-to-use-race-ethnicity-in-causal-inference | <p>It seems that often in social science, race is examined in causal terms, as researchers are interested in the differences between various ethnic groups in outcomes when controlling for other covariates. However, my understanding is that we actually can't use race for causal inference due to the omitted variable bias... | <p>Race and ethnicity are variables that cannot be "controlled" in experiments, since it is not possible for the researcher to assign or change this characteristic of the study participant.<span class="math-container">$^\dagger$</span> For this reason, causal inference relating to race and ethnicity cannot g... | 757 |
causal inference | How to understand and model Causal Inference from regression? | https://stats.stackexchange.com/questions/549892/how-to-understand-and-model-causal-inference-from-regression | <p>I'm fairly new to casual inferences. I know that regression is used to identify linear relationship between the dependent and independent variables and it doesn't necessarily mean causality.</p>
<p>I have recently come across some quasi experimental methods such as Diff-in-Diff and PSM methods which use regression t... | 758 | |
causal inference | How does BART (Bayesian Additive regression tree) help with causal inference? | https://stats.stackexchange.com/questions/446416/how-does-bart-bayesian-additive-regression-tree-help-with-causal-inference | <p>I have recently learned about using BART for causal inference from observational studies. So, I read that if we want to see the causal effect of a variable Z (binary) on Y in presence of X covariates then we can get factual (putting Z=0 for all points) and counterfactual (putting Z=1 for all points) predictions for ... | <p>BART is a regression method, just like generalized linear models (e.g., linear or logistic regression), decision trees, or many other machine learning methods. BART has a few advantages for causal inference that distinguish it from other methods.</p>
<p>First, because the tuning parameters correspond to Bayesian pr... | 759 |
causal inference | Is this a breach of the consistency principle in causal inference? | https://stats.stackexchange.com/questions/610079/is-this-a-breach-of-the-consistency-principle-in-causal-inference | <p>My understanding of the consistency principle is that the observed outcome is equal to the potential outcome. i.e. let T = treatment, if T=1 then then the Observed outcome (Y) is equal to the potential outcome i.e. Y(1) = Y . This implies that there can't be 'multiple versions of the same treatment' which will lead ... | 760 | |
causal inference | Causal Inference for Pandemic Impact on Energy Fraud (All Units Treated) | https://stats.stackexchange.com/questions/661099/causal-inference-for-pandemic-impact-on-energy-fraud-all-units-treated | <p>I'm writing my MSc thesis and need some help understanding how to make a causal estimation of the COVID-19 pandemic's impact on energy fraud.</p>
<p><strong>Context:</strong> I have a dataset of commercial losses, also known as non-technical losses, reported monthly by different energy distributors in Brazil. These... | 761 | |
causal inference | Should I use causal inference for this usecase? | https://stats.stackexchange.com/questions/605368/should-i-use-causal-inference-for-this-usecase | <p>I have a historical dataset of several million sales, and some of them are marked as returned. I have multiple variables, such as product, customers, creation date, etc. My goal is to determine the cause of the returned orders, such as whether it's a combination of a particular product type with a specific customer.... | 762 | |
causal inference | Bayesian Methods for Causal Inference with Observational Panel Data | https://stats.stackexchange.com/questions/633722/bayesian-methods-for-causal-inference-with-observational-panel-data | <p>How comprehensive is the toolkit for Bayesian inference when trying to make causal inferences with observational panel data?</p>
<p>I can see an easy application with the incorporation of fixed effects or the ADL model, but these models have well-documented problems.</p>
<p>I also understand that there are Bayesian ... | 763 | |
causal inference | How to deal with cross-elasticity and time series for optimal pricing with causal inference? | https://stats.stackexchange.com/questions/623406/how-to-deal-with-cross-elasticity-and-time-series-for-optimal-pricing-with-causa | <p>I have a problem in which the prices of an "item" will change for specific hours of the day. I was leveraging the concept of price elasticity, which includes the self- and cross-elasticity coefficients (which are not directly observed), to evaluate the impact of that change.</p>
<p>As there are can be othe... | 764 | |
causal inference | Why does propensity score matching work for causal inference? | https://stats.stackexchange.com/questions/206748/why-does-propensity-score-matching-work-for-causal-inference | <p>Propensity score matching is used for make causal inferences in observational studies (see the <a href="http://faculty.smu.edu/Millimet/classes/eco7377/papers/rosenbaum%20rubin%2083a.pdf" rel="noreferrer">Rosenbaum / Rubin paper</a>). What's the simple intuition behind why it works?</p>
<p>In other words, why if we... | <p>I'll try to give you an intuitive understanding with minimal emphasis on the mathematics. </p>
<p>The main problem with observational data and analyses that stem from it is confounding. Confounding occurs when a variable affects not only the treatment assigned but also the outcomes. When a randomized experiment ... | 765 |
causal inference | Is there relationship between propensity score based causal inference and sampling weights? | https://stats.stackexchange.com/questions/622563/is-there-relationship-between-propensity-score-based-causal-inference-and-sampli | <p>Consider observational study with single outcome <span class="math-container">$Y$</span>, single covariate <span class="math-container">$X$</span> and treatment assignment variable <span class="math-container">$W$</span>. Under unconfounded treatment assignment assumption, <span class="math-container">$E_{sp}[Y(1)]=... | 766 | |
causal inference | How come the BART results are this good at the 2016 Atlantic causal inference competition? | https://stats.stackexchange.com/questions/470754/how-come-the-bart-results-are-this-good-at-the-2016-atlantic-causal-inference-co | <p>The famous paper <a href="https://arxiv.org/abs/1707.02641" rel="noreferrer">Dorie,2017</a> shows that BART performs dramatically well in causal inference. In my replication, MSE in BART can be 40% lower than MSE in other machine learning methods.</p>
<p>But all machine learning methods just regress <span class="ma... | 767 | |
causal inference | Clarification on Counterfactual Outcomes in Causal Inference | https://stats.stackexchange.com/questions/652874/clarification-on-counterfactual-outcomes-in-causal-inference | <p>I’m studying <a href="https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/" rel="noreferrer">the textbook <em>Causal Inference: What If</em> by Miguel A. Hernán, James M. Robins</a>. On page 4, I came across a passage that seems nonsensical. The authors claim that, for each individual, the counterfactua... | <p>Different fields sometimes adopt different terminology for the same concepts. This can be very annoying when you read papers from other fields, so I might be biased in favour of “potential outcomes” simply because that’s the term used within my own field (economics).</p>
<p>That being said, the third paragraph</p>
... | 768 |
causal inference | Causal inference where potential outcome is somehow "violated"? | https://stats.stackexchange.com/questions/555975/causal-inference-where-potential-outcome-is-somehow-violated | <p>The fundamental problem of causal inference says that only one potential outcome is observed for each unit.</p>
<p>What happens if both outcomes from control and treatment can be observed? Can we still make use of analysis tools like causal trees to understand heterogeneous treatment effects?</p>
<p>As a concrete ex... | <p>I agree that there is some confusion about the "unit" of analysis here. It's neither the ad nor the viewer, though; it's the instance of showing an ad to a viewer. And there is only one potential outcome observed because that instance can only either have an image or not. Because you randomly assigned, you... | 769 |
causal inference | What are downsides to "genetic matching," particularly outside of causal inference settings? | https://stats.stackexchange.com/questions/645101/what-are-downsides-to-genetic-matching-particularly-outside-of-causal-inferen | <p>Multivariate matching methods typically involve two steps. First the user computes <span class="math-container">$D$</span>, a matrix of the multivariate distances between units. Second, the user applies a matching function (e.g., 1:1 nearest neighbor) to input <span class="math-container">$D$</span> to generate matc... | <p>It's worth remembering that genetic matching is not a matching method (despite its name); it is a method of computing the distance between units that is supplied to a matching method for use in creating groups similar on the distributions of covariates. I describe genetic matching in my blog post <a href="https://ng... | 770 |
causal inference | causal inference exercise - "covariate-specific effect" | https://stats.stackexchange.com/questions/523729/causal-inference-exercise-covariate-specific-effect | <p><a href="https://i.sstatic.net/4AjuN.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/4AjuN.png" alt="enter image description here" /></a></p>
<p>This graph and questions come from: <em>CAUSAL INFERENCE IN STATISTICS A Primer</em> - Pearl Glymour and Jewell (2016).</p>
<p>We are interested in the effec... | <p><span class="math-container">$\newcommand{\op}[1]{\operatorname{#1}}
\newcommand{\doop}{\op{do}}$</span>
Here's my answer.</p>
<p><strong>a.</strong> To get the <span class="math-container">$c$</span>-specific effect of <span class="math-container">$X$</span> and <span class="math-container">$Y,$</span> which is <sp... | 771 |
causal inference | Why do we do matching for causal inference vs regressing on confounders? | https://stats.stackexchange.com/questions/544926/why-do-we-do-matching-for-causal-inference-vs-regressing-on-confounders | <p>I'm new to the area of causal inference. From what I understand, one of the main concerns that causal inference tries to address is the effect of confounders!</p>
<p>For the sake of reference, let's denote the feature that we are interested in (a.k.a treatment or exposure) by <strong>A</strong>, other features by <s... | <p>As I see it, there are two related reasons to consider matching instead of regression. The first is assumptions about functional form, and the second is about proving to your audience that functional form assumptions do not affect the resulting effect estimate. The first is a statistical matter and the second is epi... | 772 |
causal inference | Causal Inference of Between-Group Differences in Time Series Data | https://stats.stackexchange.com/questions/476700/causal-inference-of-between-group-differences-in-time-series-data | <p>I'm relatively new to time series data/causal inference (am working my way through Mostly Harmless Econometrics as we speak). Though, I'm still not sure how to appropriately test between-group differences in time series.</p>
<p>Basically, I want to test if the "red group" is statistically different from th... | 773 | |
causal inference | What are some current research areas of interest in machine learning and causal inference? | https://stats.stackexchange.com/questions/328602/what-are-some-current-research-areas-of-interest-in-machine-learning-and-causal | <p>I am wondering if anyone has any references or material that relates to a survey or summary of current topics of research either in machine learning or at the intersection of machine learning and causal inference. </p>
| <p>Susan Athey and Guido Imbens have kindly put their lecture notes for the various 2018 causal ML courses they have taught on <a href="https://drive.google.com/drive/mobile/folders/1SEEOMluxBcSAb_tsDYgcLFtOQaeWtkLp?usp=drive_open" rel="nofollow noreferrer">a public Google Drive folder</a>. </p>
| 774 |
causal inference | How to do Causal Inference for Observational Data [Supply Cain]? | https://stats.stackexchange.com/questions/594330/how-to-do-causal-inference-for-observational-data-supply-cain | <p><strong>Problem statement:</strong> Understand what factors impact the different operational times in a supply chain warehouse operation. I have observational data (past 1 year) which contains number of orders, packages, products, time taken to push out a product out of warehouse etc.</p>
<p>I want to create a model... | 775 | |
causal inference | Using a Bayesian Additive Regression Trees model for causal inference | https://stats.stackexchange.com/questions/521925/using-a-bayesian-additive-regression-trees-model-for-causal-inference | <h1><strong>Some Context:</strong></h1>
<p>I've read this <a href="https://cds.nyu.edu/wp-content/uploads/2014/04/causal-and-data-science-and-BART.pdf" rel="nofollow noreferrer">presentation</a> about using a BART model to find out the causal effect of a certain variable with respect to a target variable (say, how much... | <p>BART is a method of estimating <span class="math-container">$E[E[Y|A=1,X]] - E[E[Y|A=0,X]]$</span> in a highly flexible way, where <span class="math-container">$Y$</span> is the outcome, <span class="math-container">$A$</span> is the treatment, and <span class="math-container">$X$</span> are covariates. BART is one ... | 776 |
causal inference | Econometrics: What are the assumptions of logistic regression for causal inference? | https://stats.stackexchange.com/questions/357915/econometrics-what-are-the-assumptions-of-logistic-regression-for-causal-inferen | <p>I'm trying to understand what are the assumptions for logistic regression when you intend to interpret the parameter as causal? The assumptions for causal OLS regressions is well-known but I can't find a good source for similar assumptions for logistic regressions.</p>
<p>From what I can find on the internet, I thi... | <p>The capacity to interpret regression relationships as causal generally depends on experimental protocols rather than the assumed structure of the statistical model. Regression models allow us to relate the explanatory variables statistically to the response variable, where this relationship is made conditional on a... | 777 |
causal inference | What are the best empirical studies comparing causal inference with experimental, quasi-experimental, and non-experimental techniques? | https://stats.stackexchange.com/questions/142212/what-are-the-best-empirical-studies-comparing-causal-inference-with-experimental | <p>The Issue: People attempt to draw causal inferences using many different statistical techniques (e.g. regression, propensity score matching, regression discontinuity, instrumental variables, etc.). One great way to learn about the strengths and weaknesses of different statistical techniques for causal inference is ... | <p>The type of study you are referring to is called a within-study comparison. An early example that produced a lot of discussion is <a href="http://users.nber.org/~rdehejia/papers/dehejia_wahba_jasa.pdf" rel="nofollow">Dehejia and Wahba</a> (1999; JASA) using Lalonde's (1986) NSW data in which they compared the result... | 778 |
causal inference | when are rational expectations a threat to causal inference? | https://stats.stackexchange.com/questions/466872/when-are-rational-expectations-a-threat-to-causal-inference | <p>Consider the impact government policy has had on deaths from COVID19. I think the potential relationships are </p>
<p><a href="https://i.sstatic.net/2Sp8S.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/2Sp8S.png" alt="enter image description here"></a></p>
<p>If the relationships are as given in t... | <p>I think I would add another node for policy at <span class="math-container">$t+1,$</span> so as not to violate the fundamental law of cause and effect (causes must precede effects). Let <span class="math-container">$E(t)$</span> be the epidemic at <span class="math-container">$t,$</span> <span class="math-container"... | 779 |
causal inference | Order in which covariates are measured in an observational study - causal inference | https://stats.stackexchange.com/questions/656860/order-in-which-covariates-are-measured-in-an-observational-study-causal-infere | <p>I want to model hba1c levels for a group of type 1 diabetes patients. I have data which are extracted from a register, and my goal is to answer whether a treatment intervention decreases hba1c levels on average. I am (trying) to use causal inference, where the average treatment effect is calculated using potential o... | <p>If I understand your setup correctly, you’re working with a binary point exposure variable <span class="math-container">$A$</span>, an outcome <span class="math-container">$Y$</span>, and an adjustment set <span class="math-container">$\{W, X\}$</span> that is sufficient for blocking confounding based on either back... | 780 |
causal inference | Rather Than Framing Causal Inference as "How Much X Causes Y to Change", Can You Frame Causal Inference As "X Explains _% of the Variation in Y" | https://stats.stackexchange.com/questions/650526/rather-than-framing-causal-inference-as-how-much-x-causes-y-to-change-can-you | <p>Most causal research designs seek to estimate a causal effect and interpret that causal effect as a marginal effect (a 1-unit shift in X leads to a _ amount of change in Y).</p>
<p>However, as I've spent more time applying causal inference practices in industry, it seems like stakeholders want to know more than just... | 781 | |
causal inference | Pearl, Causal Inference in Statistics Q3.5.1 (Backdoor criterion) | https://stats.stackexchange.com/questions/582291/pearl-causal-inference-in-statistics-q3-5-1-backdoor-criterion | <p>This is a question about backdoor criterion (as per J. Pearl) on finding causal effects. It is linked to a specific exercise in a specific book, but I hope it will be sufficiently generic and self-contained to be of general use.</p>
<h2>Problem statement</h2>
<p>I am self-studying Pearl, Glymour, Jewell <em>Causal I... | <p>No you were right to begin with, you can control for any variable along the back door path so long as it doesn’t open up new such paths.</p>
<p>You can try it for yourself for the specific diagram here (set Z to adjusted and some other one to see only the causal path remain colored): <a href="http://dagitty.net/dags... | 782 |
causal inference | Eye disease counterfactual example from Elements of Causal Inference | https://stats.stackexchange.com/questions/663641/eye-disease-counterfactual-example-from-elements-of-causal-inference | <p>I'm reading this example from <em>Elements of Causal Inference</em> by Peters, Janzing, and Schölkopf.</p>
<hr />
<p><strong>Example 3.4 (Eye disease)</strong></p>
<p>There exists a rather effective treatment for an eye
disease. For 99% of all patients, the treatment works and the patient gets cured <span class="mat... | <p>None of the terms in the structural model are probabilities; they are all indicator functions. It is simply a compact way to write the following function:
<span class="math-container">$$
B :=
\begin{cases}
1, & \text{if $T=1,N_B=1$} \\
1, & \text{if $T=0,N_B=0$} \\
0, & \text{if $T=1,N_B=0$} \\
0, ... | 783 |
causal inference | How do I interpret the identification step logs in Causal Inference using DoWhy? | https://stats.stackexchange.com/questions/623285/how-do-i-interpret-the-identification-step-logs-in-causal-inference-using-dowhy | <p>I am running Causal Inference to determine whether the mass of a vehicle affects the Co2 emissions. I understand that DoWhy follows a particular structure that is modeling-> identification -> estimation -> refutations. I was logging the outputs of each step in Python. I am having trouble understanding and i... | 784 | |
causal inference | How to determine an appropriate "closeness" threshold when matching for causal inference? | https://stats.stackexchange.com/questions/489880/how-to-determine-an-appropriate-closeness-threshold-when-matching-for-causal-i | <p>Say I have a [yes/no] treatment variable (e.g. the customer complained about their order) and I want to estimate the causal impact of this "treatment" on the average customer's future spend. To do so, I match tens of thousands of observations in such a way as to minimize their Mahalanobis distance as calcu... | <p>There are two qualities on which matched samples should be assessed: covariate balance and remaining (effective) sample size. Covariate balance is the degree to which the covariate distributions are the same between the treatment groups in the matched sample. Remaining sample size is the number of units remaining af... | 785 |
causal inference | How is the counterfactual estimated in Judea Pearls book based on causal inference ? (dowhy) | https://stats.stackexchange.com/questions/580963/how-is-the-counterfactual-estimated-in-judea-pearls-book-based-on-causal-inferen | <p>I have started looking into causal inference, in particular Dowhy package based on Judea Pearls book of why. What i don't understand is how the counterfactual is estimated?</p>
<p>My understanding is that DoWhy package (based on judea pearl book) addresses counterfactuals by creating a bayesian graphical model (at ... | <p>The "identification" step refers to identifying an estimator for the quantity of interest and whether or not it can be computed given the causal model. For example, if there are unknown confounders, or undirected edges in a causal graph, an effect may not be identifiable.</p>
<p>A counterfactual is estimat... | 786 |
causal inference | Causal Inference with unconfoundedness | https://stats.stackexchange.com/questions/466961/causal-inference-with-unconfoundedness | <p>Consider an observational study with binary treatment. I denote treatment variable as <span class="math-container">$z_i$</span>, denote observed outcome as <span class="math-container">$y_i$</span>, denote potential outcome as <span class="math-container">$y_i(1),y_i(0)$</span>, and denote covariates as <span class=... | <p>The units in the control group provide information about the relationship between the <span class="math-container">$X$</span> and <span class="math-container">$Y(0)$</span>, because for those units, <span class="math-container">$Y$</span> (the observed outcome) is equal to <span class="math-container">$Y(0)$</span>.... | 787 |
causal inference | What is the mode of inference for frequentist IPTW estimation in the causal inference context | https://stats.stackexchange.com/questions/611834/what-is-the-mode-of-inference-for-frequentist-iptw-estimation-in-the-causal-infe | <p>In Rubin 1990, Donald Rubin describes four different modes of statistical inference for causal effects:</p>
<ol>
<li>Randomization-based tests of sharp-null hypotheses - in the tradition of Fisher, if you've got an unconfounded assignment mechanism combined with a sharp null hypothesis of no treatment effect, you co... | 788 | |
causal inference | Causal Inference After Feature Selection | https://stats.stackexchange.com/questions/502082/causal-inference-after-feature-selection | <p>I am interested in this forum's thoughts concerning the use of LASSO for feature selection in a high dimensional dataset and subsequent OLS regression to adjust for confounding on the most frequently selected variables (I'm using 100 random draws). I'm aware that feature selection does not take into account the caus... | <p>Data mining for potential predictors and causal inference don't go together well. Few problems may arise:</p>
<p><em>Identification problems:</em></p>
<ul>
<li><p><strong>Confoundness:</strong> If some unobserved common causes are still not included in the data, the estimators are still biased.</p>
</li>
<li><p><str... | 789 |
causal inference | Understanding the Intersection Between Causal and Statistical Inference | https://stats.stackexchange.com/questions/627417/understanding-the-intersection-between-causal-and-statistical-inference | <p>Assume a simple example motivating a causal research design. Say that I collect a data set on rural counties in Texas and I wish to understand if rainfall causes a change in crop sales. Working with this observational data, I run a regression, conditioning on a necessary adjustment set (to the best of my capability)... | 790 | |
causal inference | How is it reasonable that randomised controlled trials can be used to perform causal inference? | https://stats.stackexchange.com/questions/638977/how-is-it-reasonable-that-randomised-controlled-trials-can-be-used-to-perform-ca | <p>I understand that <a href="https://en.wikipedia.org/wiki/Randomized_controlled_trial" rel="noreferrer">randomised controlled trials (RCTs)</a> are used to perform causal inference, but I'm a confused about how this is reasonable. Let's say that we have a treatment, and we want to find out if this treatment "wor... | <p>The randomization of participants into different groups, eg, treatment and control. is central to the inference of causality about the treatment. The randomization makes the treatment groups closely similar, on average. Larger randomized groups are more similar.</p>
<p>High variation among the participants is essent... | 791 |
causal inference | Can autocorrelation confound causal inference? | https://stats.stackexchange.com/questions/474179/can-autocorrelation-confound-causal-inference | <p>I'm working with a weekly aggregated time series that has autocorrelation and I'm trying to find out why the trend has been decreasing by regressing other features onto - I noticed that when I use an ARIMA to account for autocorrelation, it masks some features that wouldn't have been masked from OLS.</p>
<p>In the c... | <p>You want your model to be not only theoretically adequate but also statistically adequate. For a methodological discussion on that, see the Probabilistic Reduction methodology by Aris Spanos; I have summarized it in my earlier post <a href="https://stats.stackexchange.com/questions/303887">"Effects of model sel... | 792 |
causal inference | Causal Inference using Linear Regression | https://stats.stackexchange.com/questions/442256/causal-inference-using-linear-regression | <p>I have been reading recently on fitting linear regression to evaluate causal effect of some treatment. Let's call the variable in the model representing treatment as Xj.</p>
<p>From what I have read, we need to make sure to include in the model other variables that affect <strong>both</strong> the responsible varia... | <p>The following holds generally, but the exact relationships may differ depending on the data.</p>
<p>Including a variable that is a predictor of the outcome and the treatment will reduce the bias and variance of the effect estimate. The more related to the outcome and the less related to the treatment, the more vari... | 793 |
causal inference | Causal Inference on test scores | https://stats.stackexchange.com/questions/499169/causal-inference-on-test-scores | <p>I administered a test and wanted to know if the exam scores were influenced by watching videos. The participants were randomly entered into 2 arms. I have one control arm that did not watch videos, and the second arm being the group that did watch videos. I administered a pretest, had them watch the videos, and then... | <p>I'm new to causal inference, but this sounds like a straightforward application of linear models.</p>
<p>You're interested in computing</p>
<p><span class="math-container">$$ Pr(\mbox{Score} \vert do(\mbox{Videos}) ) $$</span></p>
<p>By randomizing, you have severed any arrows in your dag from confounders to the tre... | 794 |
causal inference | How to use causal inference models when we don't know the structure of the graph? | https://stats.stackexchange.com/questions/562738/how-to-use-causal-inference-models-when-we-dont-know-the-structure-of-the-graph | <p>I have recently started reading some materials in Causal Inference. Based on readings, we assume a graph that explains the relationship between treatment, outcome, and confounders. Then, they propose some methods like inverse probability weighting to compute the ATE. In many cases, we don't have access to the graph ... | 795 | |
causal inference | What does it mean to "non-parametrically" identify a causal effect within the super-population perspective in causal inference? | https://stats.stackexchange.com/questions/405086/what-does-it-mean-to-non-parametrically-identify-a-causal-effect-within-the-su | <p>I am wondering, within the context of causal inference, what it means to "non-parametrically" identify a causal effect within the super-population perspective. For example, in Hernan/Robins Causal Inference Book Draft:</p>
<p><a href="https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2019/02/hernanrobins_v... | <p>Thank you for bringing this interesting book to our attention. Below are my two cents.</p>
<blockquote>
<p>...we will assume that counterfactual outcomes are deterministic and that
we have recorded data on every subject in a very large (perhaps
hypothetical) super-population.</p>
</blockquote>
<p>The above s... | 796 |
causal inference | Finding causal Inference from sentiment analysis | https://stats.stackexchange.com/questions/553578/finding-causal-inference-from-sentiment-analysis | <p>I am conducting a sentiment analysis on thousands of social media posts by unemployed manufacturing workers to see how online sentiment of the group members I am analyzing has changed after an announcement of an economic policy program aimed at helping that group. Specifically, I am interested in moving beyond descr... | 797 | |
causal inference | Is Sensitivity Analysis for Making Causal Inferences Only for Backdoor Adjustment? | https://stats.stackexchange.com/questions/607068/is-sensitivity-analysis-for-making-causal-inferences-only-for-backdoor-adjustmen | <p>I am wondering if sensitivity analysis for causal inference is only applicable when doing backdoor adjustment/selecting on observables.</p>
<p>Conventionally, sensitivity analysis evaluates the threat of an unknown confounder to a causal estimate in observational studies. In studies where we select on the observable... | 798 | |
causal inference | In causal inference in statistics, how do you interpret the consistency assumption in mathematical terms? | https://stats.stackexchange.com/questions/304799/in-causal-inference-in-statistics-how-do-you-interpret-the-consistency-assumpti | <p>In causal inference, the consistency assumption states that there are no multiple versions of treatment. Specifically, for a potential outcome unit $Y_i$ and a binary treatment vector $\mathbf{Z}$, </p>
<p>$$
Y_i(\mathbf{Z})=Y_i(\mathbf{Z'}) \ \ \forall \ \mathbf{Z},\mathbf{Z'}:\mathbf{Z}=\mathbf{Z'}
$$
In literatu... | <p>Let me use $X$ for the treatment, $Y$ for the observed outcome and $Y(x)$ for the potential outcome under $X = x$. </p>
<p>Consistency means that for an individual $i$, his observed outcome $Y_i$ when $X_i = x$ is his potential outcome $Y_{i}(x)$. Or, more formally:</p>
<p>$$X_i = x \implies Y_i(x) = Y_i$$</p>
<p... | 799 |
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