学术文献库

Principal stratification is a framework for making sense of causal effects conditioned on variables that themselves may have been affected by treatment. For instance, one component of an educational computer application is the availability of ``bottom-out'' hints that provide the answer. In evaluating a recent experimental evaluation against alternative programs without bottom-out hints, researchers may be interested in estimating separate average treatment effects for students who, if given the opportunity, would request bottom-out hints frequently, and for students who would not. Most principal stratification estimators rely on strong structural or modeling assumptions, and many require advanced statistical training to fit and check. In this paper, we introduce a new M-estimation principal effect estimator for one-way noncompliance based on a binary indicator. Estimates may be computed using conventional regressions (though the standard errors require a specialized sandwich formula) 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 two real-data analyses.