On finite-population Bayesian inferences for $2^K$ factorial designs with binary outcomes
Methodology
2019-01-25 v2
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 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.
Keywords
Cite
@article{arxiv.1803.04499,
title = {On finite-population Bayesian inferences for $2^K$ factorial designs with binary outcomes},
author = {Jiannan Lu},
journal= {arXiv preprint arXiv:1803.04499},
year = {2019}
}
Comments
To appear in Journal of Statistical Computation and Simulation