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Question: <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 equ... | https://stats.stackexchange.com/questions/541853/causal-inference-ignorability-and-collider |
Question: <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>medi... | https://stats.stackexchange.com/questions/541852/causal-inference-moderation-and-mediation |
Question: <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... | https://stats.stackexchange.com/questions/2245/statistics-and-causal-inference |
Question: <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 i... | https://stats.stackexchange.com/questions/233235/what-distinction-is-there-between-statistical-inference-and-causal-inference |
Question: <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 ... | https://stats.stackexchange.com/questions/541807/causal-inference-selection-bias-and-endogeneity |
Question: <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">... | https://stats.stackexchange.com/questions/432298/causal-inference-for-additive-multiple-treatments |
Question: <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 unobserv... | https://stats.stackexchange.com/questions/642327/causal-inference-and-propensity-score |
Question: <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... | https://stats.stackexchange.com/questions/626310/causal-inference-with-only-interval-uncertainty |
Question: <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 variable... | https://stats.stackexchange.com/questions/56909/what-is-the-relation-between-causal-inference-and-prediction |
Question: <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 ... | https://stats.stackexchange.com/questions/479432/regression-in-causal-inference |
Question: <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>
Answer: <p>You could also use pcalg package if you are interested i... | https://stats.stackexchange.com/questions/147443/causal-inference-from-a-cross-sectional-study-design |
Question: <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 approa... | https://stats.stackexchange.com/questions/565090/machine-learning-for-causal-inference |
Question: <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 emp... | https://stats.stackexchange.com/questions/620053/bias-variance-tradeoff-in-prediction-versus-causal-inference |
Question: <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... | https://stats.stackexchange.com/questions/535388/causal-inference-for-continuous-exposures |
Question: <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 observat... | https://stats.stackexchange.com/questions/398740/aic-for-causal-inference |
Question: <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 ... | https://stats.stackexchange.com/questions/518475/time-length-for-causal-inference-experiments |
Question: <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... | https://stats.stackexchange.com/questions/419136/causal-inference-in-mortality-rates |
Question: <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 a... | https://stats.stackexchange.com/questions/645660/causal-inference-meta-learners-usage |
Question: <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 suggest... | https://stats.stackexchange.com/questions/560761/exchangeability-causal-inference-and-partial-pooling |
Question: <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-Fi... | https://stats.stackexchange.com/questions/617895/where-does-multilevlel-modeling-fit-in-with-causal-inference |
Question: <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, whi... | https://stats.stackexchange.com/questions/546510/variable-type-name-in-causal-inference |
Question: <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">$$
Y^{obs}_i=\alpha+\tau W_i+\beta X+\epsilon_i
$$</... | https://stats.stackexchange.com/questions/601289/regression-methods-in-causal-inference |
Question: <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>Give... | https://stats.stackexchange.com/questions/631678/causal-inference-on-time-series-data-is-intervention-needed |
Question: <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 th... | https://stats.stackexchange.com/questions/555626/causal-inference-short-time-series |
Question: <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... | https://stats.stackexchange.com/questions/638811/a-short-video-to-explain-causal-inference-to-non-technical-audiences |
Question: <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></... | https://stats.stackexchange.com/questions/604373/do-calculus-and-causal-inference-for-continuous-random-variables |
Question: <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 v... | https://stats.stackexchange.com/questions/464470/why-isnt-causal-inference-a-simple-specialized-regression-problem |
Question: <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>
<blo... | https://stats.stackexchange.com/questions/582471/positivity-assumption-in-causal-inference-with-continuous-covariates |
Question: <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 relatio... | https://stats.stackexchange.com/questions/384330/is-causal-inference-only-from-data-possible |
Question: <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 ab... | https://stats.stackexchange.com/questions/618819/equation-3-6-elements-of-causal-inference |
Question: <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... | https://stats.stackexchange.com/questions/548929/textbook-recommendations-covering-machine-learning-techniques-for-causal-inferen |
Question: <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... | https://stats.stackexchange.com/questions/565808/why-should-we-care-about-dags-for-causal-inference |
Question: <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 ... | https://stats.stackexchange.com/questions/561704/should-predictive-analysis-be-tackled-with-causal-inference-in-mind |
Question: <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="noreferr... | https://stats.stackexchange.com/questions/3893/can-cross-validation-be-used-for-causal-inference |
Question: <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 infere... | https://stats.stackexchange.com/questions/431939/matching-vs-simple-regression-for-causal-inference |
Question: <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 fo... | https://stats.stackexchange.com/questions/573032/causal-inference-for-experiment |
Question: <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 fram... | https://stats.stackexchange.com/questions/62025/online-resources-for-philosophy-of-causation-for-causal-inference |
Question: <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 'Causa... | https://stats.stackexchange.com/questions/576913/what-should-i-study-after-finishing-causal-inference-in-statistics-a-primer |
Question: <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 coeffic... | https://stats.stackexchange.com/questions/617367/why-use-causal-inference-if-coefficients-are-same-in-an-ols |
Question: <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... | https://stats.stackexchange.com/questions/260677/correlation-regression-and-causal-inference |
Question: <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... | https://stats.stackexchange.com/questions/458211/causal-inference-using-regression-for-multiple-covariates |
Question: <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... | https://stats.stackexchange.com/questions/565783/how-do-we-select-model-for-causal-inference |
Question: <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 ... | https://stats.stackexchange.com/questions/631817/why-do-we-need-a-consistency-assumption-in-causal-inference |
Question: <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 conditiona... | https://stats.stackexchange.com/questions/617240/in-what-cases-is-identification-not-possible-in-causal-inference |
Question: <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 th... | https://stats.stackexchange.com/questions/632249/causal-inference-propensity-score-balancing-sufficient-for-potential-outcome-b |
Question: <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 c... | https://stats.stackexchange.com/questions/637141/conditional-expectation-function-and-causal-inference |
Question: <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.
Sin... | https://stats.stackexchange.com/questions/605148/portfolio-optimization-using-causal-inferences |
Question: <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 outco... | https://stats.stackexchange.com/questions/558195/difference-between-exchangeability-and-independence-in-causal-inference |
Question: <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 me... | https://stats.stackexchange.com/questions/347899/non-obvious-real-world-datasets-for-observational-causal-inference |
Question: <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... | https://stats.stackexchange.com/questions/602716/pearls-causal-inference-in-statistics-equation-3-11-calculation-of-group-spe |
Question: <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 intere... | https://stats.stackexchange.com/questions/187008/is-there-sense-in-applying-causal-inference-methods-to-variables-with-low-correl |
Question: <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 ... | https://stats.stackexchange.com/questions/554690/bayesian-networks-vs-traditional-stats-approaches-to-causal-inference |
Question: <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 20... | https://stats.stackexchange.com/questions/656592/causal-inference-of-impact-of-a-university-bankruptcy |
Question: <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 OL... | https://stats.stackexchange.com/questions/520389/is-the-emmeans-r-package-performing-causal-inference-g-computation |
Question: <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 ... | https://stats.stackexchange.com/questions/623100/variable-selection-in-causal-inference-regression-models-when-p-n |
Question: <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 exp... | https://stats.stackexchange.com/questions/222939/what-are-key-papers-discussing-causal-inference-from-a-missing-data-perspective |
Question: <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... | https://stats.stackexchange.com/questions/545054/causal-inference-in-python-where-to-start |
Question: <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 var... | https://stats.stackexchange.com/questions/366301/when-is-it-valid-to-use-race-ethnicity-in-causal-inference |
Question: <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 re... | https://stats.stackexchange.com/questions/549892/how-to-understand-and-model-causal-inference-from-regression |
Question: <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) predic... | https://stats.stackexchange.com/questions/446416/how-does-bart-bayesian-additive-regression-tree-help-with-causal-inference |
Question: <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 ... | https://stats.stackexchange.com/questions/610079/is-this-a-breach-of-the-consistency-principle-in-causal-inference |
Question: <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 Braz... | https://stats.stackexchange.com/questions/661099/causal-inference-for-pandemic-impact-on-energy-fraud-all-units-treated |
Question: <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... | https://stats.stackexchange.com/questions/605368/should-i-use-causal-inference-for-this-usecase |
Question: <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... | https://stats.stackexchange.com/questions/633722/bayesian-methods-for-causal-inference-with-observational-panel-data |
Question: <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 c... | https://stats.stackexchange.com/questions/623406/how-to-deal-with-cross-elasticity-and-time-series-for-optimal-pricing-with-causa |
Question: <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,... | https://stats.stackexchange.com/questions/206748/why-does-propensity-score-matching-work-for-causal-inference |
Question: <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">$E_{... | https://stats.stackexchange.com/questions/622563/is-there-relationship-between-propensity-score-based-causal-inference-and-sampli |
Question: <p>The famous paper <a href="https://arxiv.org/abs/1707.02641" rel="noreferrer">Dorie,2017</a> shows that BART performs dramatically well in causal inference. In my replication, MSE in BART can be 40% lower than MSE in other machine learning methods.</p>
<p>But all machine learning methods just regress <span... | https://stats.stackexchange.com/questions/470754/how-come-the-bart-results-are-this-good-at-the-2016-atlantic-causal-inference-co |
Question: <p>I’m studying <a href="https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/" rel="noreferrer">the textbook <em>Causal Inference: What If</em> by Miguel A. Hernán, James M. Robins</a>. On page 4, I came across a passage that seems nonsensical. The authors claim that, for each individual, the cou... | https://stats.stackexchange.com/questions/652874/clarification-on-counterfactual-outcomes-in-causal-inference |
Question: <p>The fundamental problem of causal inference says that only one potential outcome is observed for each unit.</p>
<p>What happens if both outcomes from control and treatment can be observed? Can we still make use of analysis tools like causal trees to understand heterogeneous treatment effects?</p>
<p>As a c... | https://stats.stackexchange.com/questions/555975/causal-inference-where-potential-outcome-is-somehow-violated |
Question: <p>Multivariate matching methods typically involve two steps. First the user computes <span class="math-container">$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 gen... | https://stats.stackexchange.com/questions/645101/what-are-downsides-to-genetic-matching-particularly-outside-of-causal-inferen |
Question: <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... | https://stats.stackexchange.com/questions/523729/causal-inference-exercise-covariate-specific-effect |
Question: <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 feat... | https://stats.stackexchange.com/questions/544926/why-do-we-do-matching-for-causal-inference-vs-regressing-on-confounders |
Question: <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 differe... | https://stats.stackexchange.com/questions/476700/causal-inference-of-between-group-differences-in-time-series-data |
Question: <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>
Answer: <p>Susan Athey and Guido Imbens have kindly put their lecture notes for the var... | https://stats.stackexchange.com/questions/328602/what-are-some-current-research-areas-of-interest-in-machine-learning-and-causal |
Question: <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 crea... | https://stats.stackexchange.com/questions/594330/how-to-do-causal-inference-for-observational-data-supply-cain |
Question: <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... | https://stats.stackexchange.com/questions/521925/using-a-bayesian-additive-regression-trees-model-for-causal-inference |
Question: <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 inter... | https://stats.stackexchange.com/questions/357915/econometrics-what-are-the-assumptions-of-logistic-regression-for-causal-inferen |
Question: <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 inf... | https://stats.stackexchange.com/questions/142212/what-are-the-best-empirical-studies-comparing-causal-inference-with-experimental |
Question: <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 ... | https://stats.stackexchange.com/questions/466872/when-are-rational-expectations-a-threat-to-causal-inference |
Question: <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 p... | https://stats.stackexchange.com/questions/656860/order-in-which-covariates-are-measured-in-an-observational-study-causal-infere |
Question: <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... | https://stats.stackexchange.com/questions/650526/rather-than-framing-causal-inference-as-how-much-x-causes-y-to-change-can-you |
Question: <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 <e... | https://stats.stackexchange.com/questions/582291/pearl-causal-inference-in-statistics-q3-5-1-backdoor-criterion |
Question: <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 ... | https://stats.stackexchange.com/questions/663641/eye-disease-counterfactual-example-from-elements-of-causal-inference |
Question: <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 understan... | https://stats.stackexchange.com/questions/623285/how-do-i-interpret-the-identification-step-logs-in-causal-inference-using-dowhy |
Question: <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 distanc... | https://stats.stackexchange.com/questions/489880/how-to-determine-an-appropriate-closeness-threshold-when-matching-for-causal-i |
Question: <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 ... | https://stats.stackexchange.com/questions/580963/how-is-the-counterfactual-estimated-in-judea-pearls-book-based-on-causal-inferen |
Question: <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 <s... | https://stats.stackexchange.com/questions/466961/causal-inference-with-unconfoundedness |
Question: <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 effe... | https://stats.stackexchange.com/questions/611834/what-is-the-mode-of-inference-for-frequentist-iptw-estimation-in-the-causal-infe |
Question: <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 accoun... | https://stats.stackexchange.com/questions/502082/causal-inference-after-feature-selection |
Question: <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 c... | https://stats.stackexchange.com/questions/627417/understanding-the-intersection-between-causal-and-statistical-inference |
Question: <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... | https://stats.stackexchange.com/questions/638977/how-is-it-reasonable-that-randomised-controlled-trials-can-be-used-to-perform-ca |
Question: <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>
<... | https://stats.stackexchange.com/questions/474179/can-autocorrelation-confound-causal-inference |
Question: <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 respons... | https://stats.stackexchange.com/questions/442256/causal-inference-using-linear-regression |
Question: <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... | https://stats.stackexchange.com/questions/499169/causal-inference-on-test-scores |
Question: <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 ... | https://stats.stackexchange.com/questions/562738/how-to-use-causal-inference-models-when-we-dont-know-the-structure-of-the-graph |
Question: <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/hern... | https://stats.stackexchange.com/questions/405086/what-does-it-mean-to-non-parametrically-identify-a-causal-effect-within-the-su |
Question: <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 be... | https://stats.stackexchange.com/questions/553578/finding-causal-inference-from-sentiment-analysis |
Question: <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 ... | https://stats.stackexchange.com/questions/607068/is-sensitivity-analysis-for-making-causal-inferences-only-for-backdoor-adjustmen |
Question: <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>$$
Y_i(\mathbf{Z})=Y_i(\mathbf{Z'}) \ \ \forall \ \mathbf{Z},\mathbf{Z'}:\mathbf{Z}=\mathbf{Z'}
$$
I... | https://stats.stackexchange.com/questions/304799/in-causal-inference-in-statistics-how-do-you-interpret-the-consistency-assumpti |
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