Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
Methodology
2022-09-26 v1 Machine Learning
Abstract
What is the ideal regression (if any) for estimating average causal effects? We study this question in the setting of discrete covariates, deriving expressions for the finite-sample variance of various stratification estimators. This approach clarifies the fundamental statistical phenomena underlying many widely-cited results. Our exposition combines insights from three distinct methodological traditions for studying causal effect estimation: potential outcomes, causal diagrams, and structural models with additive errors.
Keywords
Cite
@article{arxiv.2209.11400,
title = {Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations},
author = {P. Richard Hahn and Andrew Herren},
journal= {arXiv preprint arXiv:2209.11400},
year = {2022}
}