Statistical Treatment Choice Based on Asymmetric Minimax Regret Criteria
This paper studies the problem of treatment choice between a status quo treatment with a known outcome distribution and an innovation whose outcomes are observed only in a representative finite sample. I evaluate statistical decision rules, which are functions that map sample outcomes into the planner’s treatment choice for the population, based on regret, which is the expected welfare loss due to assigning inferior treatments. I extend previous work that applied the minimax regret criterion to treatment choice problems by considering decision criteria that asymmetrically treat Type I regret (due to mistakenly choosing an inferior new treatment) and Type II regret (due to mistakenly rejecting a superior innovation). I derive exact finite sample solutions to these problems for experiments with normal, Bernoulli and bounded distributions of individual outcomes. In conclusion, I discuss approaches to the problem for other classes of distributions. Along the way, the paper compares asymmetric minimax regret criteria with statistical decision rules based on classical hypothesis tests.
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