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Non-compliance is common in real world experiments. We focus on inferenceabout the sample complier average causal effect, that is, the average treatmenteffect for experimental units who are compliers. We present three types ofinference strategies for the sample complier average causal effect: the Waldestimator, regression adjustment estimators and model-based Bayesian inference.Because modern computer assisted experimental designs have been used to improvecovariate balance over complete randomization, we discuss inference under bothcomplete randomization and a specific computer assisted experimental design -Mahalanobis distance based rerandomization, under which asymptotic propertiesof the Wald estimator and regression adjustment estimators can be derived. Weuse Monte Carlo simulation to compare the finite sample performance of themethods under both experimental designs. We find that under either design, theBayesian method performs the best because it is stable, it yields smallestmedian absolute error and smallest median interval length. The improvement bythe Bayesian method is especially large when the fraction of compliers issmall. We present an application to a job training experiment withnon-compliance.

Inference of Sample Complier Average Causal Effects under Experiments  with Completely Randomized Design and Computer Assisted Balance-Improving  Designs