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Model-Free Causal Inference of Binary Experimental Data
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Peng Ding; Luke W. Miratrix – Grantee Submission, 2019
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…
Descriptors: Causal Models, Statistical Inference, Randomized Controlled Trials, Bayesian Statistics