Regression assisted inference for the average treatment effect in paired experiments

TL;DR

This paper introduces regression-assisted estimators for the average treatment effect in paired experiments, improving inference accuracy and efficiency even with covariate imbalances and model misspecification.

Contribution

It proposes two new regression-based estimators that are consistent and asymptotically conservative, enhancing analysis of paired experiments without strict model assumptions.

Findings

Regression-assisted estimators have smaller or equal variance compared to standard methods.

The estimators are consistent even if the regression model is misspecified.

Simulation studies show improved performance in small and moderate samples.

Abstract

In paired randomized experiments individuals in a given matched pair may differ on prognostically important covariates despite the best efforts of practitioners. We examine the use of regression adjustment as a way to correct for persistent covariate imbalances after randomization, and present two regression assisted estimators for the sample average treatment effect in paired experiments. Using the potential outcomes framework, we prove that these estimators are consistent for the sample average treatment effect under mild regularity conditions even if the regression model is improperly specified. Further, we describe how asymptotically conservative confidence intervals can be constructed. We demonstrate that the variances of the regression assisted estimators are at least as small as that of the standard difference-in-means estimator asymptotically. Through a simulation study, we illustrate the appropriateness of the proposed methods in small and moderate samples. The analysis does not require a superpopulation model, a constant treatment effect, or the truth of the regression model, and hence provides a mode of inference for the sample average treatment effect with the potential to yield improvements in the power of the resulting analysis over the classical analysis without imposing potentially unrealistic assumptions.