Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
Year: 2022
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Hahn, P. Richard, Herren, Andrew
Institution: Arizona State University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4297443003