Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data

Andrew Gelman, Matthijs Vákár

It is not always clear how to adjust for control data in causal inference, balancing the goals of reducing bias and variance. We show how, in a setting with repeated experiments, B…