  Principal stratification is a framework for making sense of causal effects
conditioned on variables that themselves may have been affected by treatment.
Most principal stratification estimators rely on strong structural or modeling
assumptions, and many require advanced statistical training to fit and to
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 illustrate the new technique in an analysis of student log data from a
recent educational technology field experiment.
