  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.
