Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges

TL;DR

This paper reviews methods for estimating average treatment effects, emphasizing the importance of supplementary analyses to improve credibility, especially in high-dimensional covariate settings.

Contribution

It extends existing semiparametric estimation lessons to high-dimensional covariate contexts and advocates for comprehensive supplementary analyses.

Findings

Supplementary analyses enhance credibility of treatment effect estimates.

High-dimensional settings pose new challenges for estimation.

Reporting multiple analyses is recommended for robust inference.

Abstract

There is a large literature on semiparametric estimation of average treatment effects under unconfounded treatment assignment in settings with a fixed number of covariates. More recently attention has focused on settings with a large number of covariates. In this paper we extend lessons from the earlier literature to this new setting. We propose that in addition to reporting point estimates and standard errors, researchers report results from a number of supplementary analyses to assist in assessing the credibility of their estimates.