Causal Effect Estimation
The Neyman-Rubin Potential Outcomes Framework is an approach for estimating causal effects (also known as treatment effects) in causal inference. It defines causality through the potential outcomes \(Y\) of a binary intervention \(T\). The causal effect, defined as the difference between these potential outcomes, is the core focus of this framework. Rubin [Rub05]
However, since only one of the potential outcomes is observed—either the unit receives the intervention or it does not—the difference in potential outcomes is unobservable. This is known as the “Fundamental Problem of Causal Inference”.
Recent advancements have developed improved statistical estimators for causal effects, each associated with specific causal assumptions. This module integrates these advancements with foundational causal identification through Bayesian networks. Pearl [Pea95] It provides a pipeline for detecting suitable adjustment sets and applying the appropriate estimators to achieve accurate causal effect estimations.
Judea Pearl. Causal diagrams for empirical research. Biometrika, 82(4):669–688, 1995. URL: https://www.jstor.org/stable/2337329.
Donald B Rubin. Causal inference using potential outcomes: design, modeling, decisions. Journal of the American Statistical Association, 100(469):322–331, 2005. URL: https://www.jstor.org/stable/27590541.