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In randomized experiments, the actual treatments received by someexperimental units may differ from their treatment assignments. Thisnon-compliance issue often occurs in clinical trials, social experiments, andthe applications of randomized experiments in many other fields. Under certainassumptions, the average treatment effect for the compliers is identifiable andequal to the ratio of the intention-to-treat effects of the potential outcomesto that of the potential treatment received. To improve the estimationefficiency, we propose three model-assisted estimators for the complier averagetreatment effect in randomized experiments with a binary outcome. We studytheir asymptotic properties, compare their efficiencies with that of the Waldestimator, and propose the Neyman-type conservative variance estimators tofacilitate valid inferences. Moreover, we extend our methods and theory toestimate the multiplicative complier average treatment effect. Our analysis israndomization-based, allowing the working models to be misspecified. Finally,we conduct simulation studies to illustrate the advantages of themodel-assisted methods and apply these analysis methods in a randomizedexperiment to evaluate the effect of academic services or incentives onacademic performance.

Model-assisted complier average treatment effect estimates in randomized experiments with non-compliance and a binary outcome