  For binary experimental data, we discuss randomization-based inferential
procedures that do not need to invoke any modeling assumptions. We also
introduce methods for likelihood and Bayesian inference based solely on the
physical randomization without any hypothetical super population assumptions
about the potential outcomes. These estimators have some properties superior to
moment-based ones such as only giving estimates in regions of feasible support.
Due to the lack of identification of the causal model, we also propose a
sensitivity analysis approach which allows for the characterization of the
impact of the association between the potential outcomes on statistical
inference.
