GEEPERs: Principal Stratification using Principal Scores and Stacked Estimating Equations

TL;DR

This paper introduces a new, robust estimator for principal effects in noncompliance scenarios that is accessible to applied researchers and does not rely on distributional assumptions.

Contribution

The paper presents a novel estimator for principal effects using principal scores and stacked estimating equations that is easier to implement and more robust than existing methods.

Findings

The new estimator performs better in simulations compared to popular alternatives.

Estimates can be obtained using standard regression methods with a specialized sandwich estimator for standard errors.

The method is demonstrated through a real-data analysis.

Abstract

Principal stratification is a framework for making sense of causal effects conditioned on variables that may themselves have been affected by the treatment. For instance, in an evaluation of an educational intervention, some subjects in the treatment group may not fully utilize the intervention, and researchers may be interested in how this subgroup is affected. Most principal stratification estimators rely on strong structural or modeling assumptions and often require advanced statistical training to fit and evaluate, making them inaccessible to many applied researchers. In this paper, we introduce a new principal effect estimator for one-way noncompliance based on a binary indicator. Estimates may be computed using conventional regression methods (though the standard errors require a specialized sandwich estimator) and do not rely on distributional assumptions. We present a simulation study that demonstrates the novel method's greater robustness compared to popular alternatives and illustrate the method through a real-data analysis.