Is Regression Adjustment Supported By the Neyman Model for Causal Inference?

This paper examines both theoretically and empirically whether using ordinary least squares (OLS) multivariate regression models to estimate average treatment effects under experimental designs is justified by the Neyman model for causal inference. The paper finds that estimated standard errors and significance levels for average treatment effect estimators are similar under the OLS and Neyman models when baseline covariates are included in the models, even though theory suggests that this may not have been the case.