Sharpening randomization-based causal inference for 22 factorial designs with binary outcomes.

In medical research, a scenario often entertained is randomized controlled 22 factorial design with a binary outcome. By utilizing the concept of potential outcomes, Dasgupta et al. proposed a randomization-based causal inference framework, allowing flexible and simultaneous estimations and inferences of the factorial effects. However, a fundamental challenge that Dasgupta et al.'s proposed methodology faces is that the sampling variance of the randomization-based factorial effect estimator is unidentifiable, rendering the corresponding classic "Neymanian" variance estimator suffering from over-estimation. To address this issue, for randomized controlled 22 factorial designs with binary outcomes, we derive the sharp lower bound of the sampling variance of the factorial effect estimator, which leads to a new variance estimator that sharpens the finite-population Neymanian causal inference. We demonstrate the advantages of the new variance estimator through a series of simulation studies, and apply our newly proposed methodology to two real-life datasets from randomized clinical trials, where we gain new insights.