Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations

P. Richard Hahn; Andrew Herren

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arXiv · 2209.11400

Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations

P. Richard Hahn·Andrew Herren

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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.

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P. Richard Hahn, Andrew Herren. 2022-09-23. Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations. https://arxiv.org/abs/2209.11400

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