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Principal stratification is a framework for making sense of causal effectsconditioned on variables that themselves may have been affected by treatment.Most principal stratification estimators rely on strong structural or modelingassumptions, and many require advanced statistical training to fit and tocheck. In this paper, we introduce a new M-estimation principal effectestimator for one-way noncompliance based on a binary indicator. Estimates maybe computed using conventional regressions (though the standard errors requirea specialized sandwich formula) and do not rely on distributional assumptions.We illustrate the new technique in an analysis of student log data from arecent educational technology field experiment.

GEEPERs: Principal Stratification using Principal Scores and Stacked Estimating Equations