On finite-population Bayesian inferences for 2^K factorial designs with binary outcomes

03/12/2018

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by   Jiannan Lu, et al.

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Inspired by the pioneering work of Rubin (1978), we employ the potential outcomes framework to develop a finite-population Bayesian causal inference framework for randomized controlled 2^K factorial designs with binary outcomes, which are common in medical research. As demonstrated by simulated and empirical examples, the proposed framework corrects the well-known variance over-estimation issue of the classic "Neymanian" inference framework, under various settings.