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arXiv · 1803.04499

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

Jiannan Lu
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Abstract

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.

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Jiannan Lu. 2019-01-24. On finite-population Bayesian inferences for $2^K$ factorial designs with binary outcomes. https://doi.org/10.1080/00949655.2019.1574793

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