Questions tagged [causality]

The relationship between cause and effect.

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1answer
9 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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1answer
14 views

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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1answer
249 views

Invariance of causal prediction

I am reading Causal inference by using invariant prediction: identification and confidence intervals by Jonas Peters (link to the resource is here: https://rss.onlinelibrary.wiley.com/doi/full/10.1111/...
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2answers
1k views

How does Inverse weighted propensity score regression differ from propensity score matching?

I understand that in inverse weighted propensity score regression, a set of weights are used to create scoring. In propensity score matching, a propensity score is created for each strata and people ...
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1answer
53 views

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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3answers
379 views

How to determine if two categorical variables are dependent while controlling for a 3rd categorical?

I have 3 categorical variables: country, gender, and liked (whether the user liked the content or not). Using Chi-squared I see that 'liked' is dependent on country, that 'liked' is dependent on ...
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0answers
20 views

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
348 views

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
98 views

Simpson's paradox in Judea Pearl's book?

I'm looking at the following question in Judea Pearl's primer on causality In an attempt to estimate the effectiveness of a new drug, a randomized experiment is conducted. In all, 50% of the patients ...
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1answer
33 views

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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1answer
480 views

Can I use Rolling and Window cross-validation techniques to check feature importance and forecasting error stability?

I want to propose a simple experiment. Let's say I have a time series data, where I first split data into train and test sets and then work with my training set to pick the best model to do forecast ...
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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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1answer
1k views

Relation between AR(p) stationarity and causality

Let's take an AR(p) model $\phi(L)y_t=z_t$ where $\phi(L)=1-\phi_1-...-\phi_pL^p$ and L is the lag operator. I have just studied that if there are no roots of the polynomial on the unit circle, $1/\...
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1answer
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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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1answer
96 views

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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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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0answers
18 views

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
52 views

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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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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0answers
23 views

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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0answers
16 views

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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0answers
80 views

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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1answer
22 views

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
69 views

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
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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4answers
6k views

Omitted variable bias: which predictors do I need to include, and why?

For a last couple of weeks I've been thinking about OVB (Omitted variable bias) in the context of regression and solution for that (how to avoid this problem). I am acquainted with Shalizi's lectures (...
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1answer
17 views

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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1answer
44 views

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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1answer
20 views

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
59 views

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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0answers
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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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3answers
900 views

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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2answers
205 views

Difference-in-difference: common trend

I'm new to this concept, and I'm referring to the book Mastering Metrics: The Path from Cause to Effect by Joshua D. Angrist and Jörn-Steffen Pischke. It states that an important assumption of ...
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2answers
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
47 views

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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0answers
87 views

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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1answer
21 views

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
76 views

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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1answer
126 views

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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1answer
63 views

Is the emmeans R package performing causal inference G-computation?

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() ...
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1answer
196 views

Does confounding always imply endogeneity?

I'm a bit confused with the definitions regarding causal inference. My question is whether we can call measured confounding an endogeneity problem?
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0answers
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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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0answers
25 views

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
80 views

How to best respond to investigator who wants to study secondary outcome

I'm co-investigator on a clinical trial where the we are studying the effect of an intervention. The study is powered to detect a clinically meaningful change for some primary endpoint. My questions ...
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1answer
21 views

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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2answers
7k views

Structural Equation Models (SEMs) versus Bayesian Networks (BNs)

The terminology here is a mess. "Structural equation" is about as vague as "architectural bridge" and "Bayesian network" is not intrinsically Bayesian. Even better, God-of-causality Judea Pearl says ...
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1answer
213 views

Transforming Heterogeneous Treatment Effect Models (in EconML) into Average Treatment Effect Model (from DoWhy)

This question relates to the steps one would need to take in order to reproduce an answer from the DoWhy tutorial, using the EconML library code for heterogeneous causal effects. In DoWhy, there is ...

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