Series:CCausal Inference

Causality: Mediation Analysis

Kids are the prototypical question makers; they never stop asking questions. Just after you have answered a Why? question, they ask yet another Why? This is the problem of mediation…

Causality: Probabilities of Causation

Questions of attribution are everywhere: i.e., did \(X=x\) cause \(Y=y\)? From…

Causality: Regret? Look at Effect of Treatment on the Treated

Regret about our actions stems from a counterfactual question: What if I had acted differently?. Therefore, to answer such question, we need a more elaborate language than the one we…

Causality: Counterfactuals - Clash of Worlds

We’ve seen how the language of causality require an exogenous intervention on the values of \(X\); so far…

Causality: Testing Identifiability

We’ve defined causal effects as an interventional distribution and posit two identification strategies to estimate them: the back-door and the front-door criteria. However, we cannot always use these…

Causality: The front-door criterion

In a past blogpost, I’ve explore the backdoor criterion: a simple graphical algorithm, we can define which variables we must include in our analysis in order to cancel out all the information coming from different causal relationships than the one…

Causality: To adjust or not to adjust

In this blogpost, I’ll simulate data to show how conditioning on as many variables as possible is not a good idea. Sometimes, conditioning can lead to de-confound an effect; other times, however, conditioning on a variable can create unnecessary…

Causality: Invariance under Interventions

In the last post we saw how two causal models can yield the same testable implications and thus cannot be distinguished from data alone. That is, we cannot gain…

Causality: Bayesian Networks and Probability Distributions

Stats people know that correlation coefficients do not imply causal effects. Yet, very often, partial correlation coefficients from regressions…