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

• URL: http://arxiv.org/abs/2209.11400v1
• Date: Fri, 23 Sep 2022 04:20:50 GMT
• Title: Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
• Authors: P. Richard Hahn, Andrew Herren
• Abstract summary: 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.
• Score: 0.456877715768796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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