Questions tagged [causality]

The relationship between cause and effect.

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10 views

Causality comes form experiment manipulation or statistical calculation?

It is known that "Regression can only imply correlation but not causality" But whether a research can draw their variables to causality relationship is somewhat determined by whether the ...
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Adding baseline covariates in stabilized weights change covariate balancing

I am computing weights using inverse probability of treatment weighting for marginal structural models (Robins et al. 2000). With both time-varying and time-invariant (baseline) covariates, some ...
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What is the correct way to calculate the covariate-specific effect in causal inference?

My question is related to the concept named "Covariate-Specific Effects" in the book "Causal Inference in Statistics: A Primer". In Section 3.3, it is called the "w-specific ...
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1answer
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How is the log likelihood calculated for bayesian networks?

In structure learning, there are score-based methods which rely on information criteria such as BIC or AIC. BIC, specifically, is defined as: $$ BIC = k \ln(n) - 2\ln\left(\hat{L}\right) $$ Where $k$ ...
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How to interpret Pearl's do notation?

I'm going through the Dragonnet paper (slides available here), and the authors use Pearl's do notation to make this claim: How can I interpret the do notation? Is the author claiming that the average ...
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1answer
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Regression for Completely Randomized Experiments

I'm reading Guido W. Imbens and Donald B. Rubin's book on Causal Inference. In chapter 7 they try to justify using a regression model to estimate the mean casual effect in a completely random ...
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1answer
30 views

Fixed effects in DAGs

Let's imagine I'm interested in studying the causal effect of beliefs in some ideas and behavior related to these ideas (say, if I believe sunscreen is good for my health, I use more sunscreen etc.). ...
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Which one of these methods ATE, ATT, ATO (overlap) should be used to evaluate a strategically applied treatment?

Lets assume I have data from a medical trial in which a new medicine was given to patients who, based on certain characteristics, doctors believed that they would benefit from this new treatment. I ...
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Omitted Variable Bias and Causal Relationships between Independent Variables

I am working on a linear modeling task and I'm trying to find the right variables to include in the model. I need to estimate whether the causal effect of one independent variable on the percentage ...
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1answer
25 views

Difference between total causal effect and average treatment effect

I'm new to causality and have a basic understanding of Average Treatment Effect (ATE). Recently, I came across Judea Pearl's definition of Total Effect/Total Causal Effect. Could anyone tell me what ...
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1answer
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How to derive the form of the central limit theorem for the difference-in-means estimator?

In randomized studies, we have that the difference-in-means estimator is given, for treatment/control as: $$ \hat{\tau}_{DM} = \frac{1}{n_1}\sum_{Z_i = 1}Y_i - \frac{1}{n_0}\sum_{Z_i = 0}Y_i $$ where $...
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Constrastive vs Counterfactual Explanations

Is there any canonical definition for Contrastive vs Counterfactual explanations? In the literature, I keep reading different versions but I wonder if there are good definitions or illustrative ...
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A doubt on different ways of performing staggered DID?

Recently, I read two papers by Dasgupta,2019 and Dong,2019 examining the impact of staggered laws in different dependent variables. When having a really long discussion with people in the comment ...
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What are the main methods for estimating the Average Treatment Effect in Observational Studies outside of matching?

I am wondering what the main methods for estimating the Average Treatment Effect in Observational Studies are outside of matching. In matching, there are weighting, stratification, propensity score ...
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1answer
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How are prerequisites/eligibility criteria defined in causal contexts?

In a causal graph (DAG), $A\to B$ means $A$ causes $B.$ Even correlation can be defined with causal relationships (for example, maybe $A$ is correlated with $B$ because $C$ causes both $A$ and $B$). ...
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When should use the word “causation” versus “causality”?

Today, I read a couple of discussions in Cross Validated, and these two words popped up quite a bit. I do not know how to differentiate them in usage (especially writing) because they are quite ...
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Testing pre-trend in DID

I follow Dasgupta, 2019 to set up the control and treatment variables for staggered implementation of laws among countries as events. The treatment and control groups are explained in one topic. I am ...
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1answer
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Visualizing time series regression results in a causal framework

Suppose I have a set of independent variables that I believe to, collectively, cause the observed level and changes in the value of the dependent variable, and I have the results of a regression of ...
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1answer
37 views

Causal inference for a trained Gradient Boosting of decision trees

I am quite new to the space, but I was wondering if it was possible to do causal analysis with a model trained with Gradient Boosting of decision trees? I know there are packages like causalnex, https:...
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1answer
35 views

Can this additional data improve the validity of Difference-in-Differences estimate

I am aiming to measure the impact of a treatment (a marketing stimulus) on product revenue. The data has resulted from a natural experiment. The set-up is tabulated below. Cells in the table indicate ...
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2answers
76 views

Identify the confidence of the impact of a proposed improvement initiative

Assume I have a manufacturing process that involves a moving train. It has failures of certain types like brakes and steering and also the weather. However, we can not do anything with regard to ...
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Does education level affect salary?

This is an interview question that I came across, and I'm not sure how to answer it. There are two ways of phrasing a causal question: If someone has a high salary, was it because they had a high ...
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Which OLS assumptions are colliders violating?

The following webpage says that: We should not control for a collider variable! Which OLS assumptions are colliders violating?
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What does counterfactual mean in Difference-in-Difference setting?

In a topic asked, the expert mentioned the word "counterfactual" [W]hy do they need to write down "adopted a leniency law at some later point of time"? Because in Korea case, the ...
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All-subsets regression and parameter shift to estimate or identify omitted variable biases?

I have multiple ($12$) predictors ($X$) for an outcome (spending) where it's likely/possible that: Some predictors are correlated Some predictors could (partially) mediate the effect of others There ...
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Size of Complier Group (Instrumental Variables) - Mostly Harmless vs. Abadie Kappa

Focusing solely on the case with a binary endogenous explanatory variable $D$ and binary instrument $Z$ and control variables $X$, Angrist & Pischke 2009 ("Mostly Harmless Econometrics") ...
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Is there a real example in which a correlation finally leads to the discovery of a non-trivial causal relationship?

More specifically, I am wondering if there is such an example satisfying the following criteria: The example happened after 1888, it would be better to be after 1900—I think few people have the ...
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Difference between an “Empirical Strategy” and an “Identification Strategy” in econometrics?

What are the substantive differences between these terms, specifically for economists? Are instrumental variables, difference-in-differences, and regression discontinuity designs considered ‘...
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173 views

Why is randomness so useful?

Where by "why" I do not mean "list of use cases for randomness." If one has a quantitative question Q about topic X, it does not seem intuitive that values that by definition have ...
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1answer
31 views

How to test for a partially mediated model?

I have a dataset with three variables: Outcome, Exposure, and Mediator. My hypothesis is that the variables are related as in the following DAG: In particular I want to test that "Mediator" ...
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What can we explain while adding region*year fixed effects, the sign flip?

In my work, I used OLS model to run the regressors on Return on Equity.And the result is as below While variable of interest is pt, which is post*treatment in difference-in-difference setting. While ...
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Equivalence of Inverse Probability of Treatment Weights and Standardization: Hernan and Robins proof

Hernan and Robins provide a proof for the equivalency of inverse probability weights and standardization for estimating the potential outcome mean that I am struggling to follow (technical point 2.3, ...
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How to prove positive effect of a variable with regression?

I am simply trying to prove that giving cash assistance to a company effects their related financial items in a good way. I calculated the related item's mean for all companies, it's non negative and ...
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1answer
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Better methodologies to make causal recommendations from correlated data?

I work as a data scientist at a SAAS company. We have an outcome variable, Y, that we consider "success" for our customers. We have a bunch of additional outcome variables X1, X2, X3 that ...
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1answer
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causal inference exercise - “covariate-specific effect”

This graph and questions come from: CAUSAL INFERENCE IN STATISTICS A Primer - Pearl Glymour and Jewell (2016). We are interested in the effect of $X$ on $Y$. In order to identify it we looking for the ...
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Instrumental Variable and “Exclusivity”

In the following DAG: Can I use IV1 as an instrument for exposure? In the this video at 4:26 the teacher explains a principle of "exclusivity" for instrumental variables. Cutoff causes ...
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1answer
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Is it complete mediation when my independent variabel has a significant effect on the dependent variable, only after adding the mediator variable?

I am currently working on my masters thesis and I am testing for mediation. For this mediation I am using the Baron and Kenny method. For path c, you need to see if the effect of the X variable (...
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1answer
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From DAG to SEM, an example

I'm dealing with the following graph, taken from Causality by Pearl (2009): The book says that in order to identify $\beta$, the coefficient of regression $X = \theta_1 Z + r_1$ is good, and for $\...
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Checking for reverse causality with lead regression, should the lead model be in its original state i.e. no transformation? [duplicate]

Im checking for reverse causality with a regression including leads. The reasoning is that the coefficient of the lead should not be significant if no problem with endogeneity. The original model is: $...
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How to choose control groups (and how many) to use in CausalImpact?

I'm using the CausalImpact package and noticed that the results are very sensitive to the choice of control groups being fed. My main questions are: Is there a recommended approach for how to choose ...
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1answer
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Is self-selection for a treatment a problem after all?

I have devised the following R code. In this simulated dataset, we know that a certain treatment (e.g. a degree, an education) increases income by $ 1,000. Income is also caused by age. In the first ...
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What are the use cases for Propensity Score Matching?

I have asked here whether, in order to establish causal relationships, the treated group and the control group must be similar on all covariates. The answer was no, if we control for the covariates in ...
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2answers
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Discover causal effects using OLS: does the treated and not treated group need to be similar on all observed variables?

I have one dummy variable, $D$, which equals 1 if the subject received treatment and $0$ otherwise. My outcome of interest is $Y$. For example, $D$ tells me whether the subject took the drug or a ...
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Does anyone have experience using causal random forests (e.g. causal_forest from grf R package)? Can they be reliably used in observational studies?

I am considering using causal random forests to detect/quantify potential causal effects of a variable of interest (e.g. radiation dose) on an outcome variable (e.g. some type of radiation-induced ...
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1answer
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What are some examples when the Average Treatment Effect on the Treated/Control (ATT,ATC) is more sought after than the ATE?

I am wondering when and why one may calculate the Average Treatment Effect on the Treated (ATT) or the Average Treatment Effect on the Control (ATC). Is there a specific example or motivation for when ...
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1answer
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Using a Bayesian Additive Regression Trees model for causal inference

Some Context: I've read this presentation about using a BART model to find out the causal effect of a certain variable with respect to a target variable (say, how much does a specific medicine ...
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Why doesn't this work as a backdoor?

In Pearl's book "Causality" on page 124 (http://bayes.cs.ucla.edu/BOOK-2K/ch3-3.pdf) he says: A set of variables $Z$ satisfies the back-door criterion relative to an ordered pair $(X_i,X_j)$...
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How to choose covariate adjustment method for effect of treatment estimation? [closed]

When trying to estimate average treatment effect (ATE) how does one choose between the different methods available to adjust for confounders? For example, how to choose between propensity score block ...
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Propensity Score Matching for Panel Data with Multinomial Outcome on Stata

I have a panel data set from 1999-2019 through which I would like to find whether a divorce would change a respondent's political opinion. I have identified a number of covariates, most of which are ...
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Effect of correlation in matching

What is the effect of correlations among observed covariates or between observed and unobserved covariates on the quality of matching, when matching iss done using Propensity score (Euclidian/...

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