Abstract:

In a randomized controlled trial (RCT), some subjects assigned to the treatment condition may not fully comply. Often there is interest in the effect of the treatment within the "principal stratum" of subjects who would comply if assigned to treatment. However, it is unknown which control subjects would have complied if treated and which wouldn't. One approach to identifying potential compliers in the control group uses "principal scores," probabilities of compliance, conditional on covariates. To use principal scores, the analyst will typically either fit a parametric finite mixture model, or assume "principal ignorability," that potential outcomes are independent of principal stratum conditional on covariates. In this talk I will introduce an semiparametric Mestimation approach based an an alternative assumption of no interactions between principal strata and covariates in an outcome regression. Unlike typical mixture modeling, this new approach makes no distributional assumptions about potential outcomes. I will evaluate the new method's properties in a simulation study and illustrate the method with data from an A/B test on an educational technology platform.
