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

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, a setting with repeated experiments, Bayesian hierarchical modeling yields an adaptive procedure that uses determine much adjustment perform. The result novel analysis increased statistical efficiency compared default based on difference estimates. demonstrate this two real examples, as well series simulated datasets. can have real-world consequences terms conclusions be drawn from experiments. also discuss relevance work inference design more generally.