Robust Bayes Treatment Choice with Partial Identification

Understand

We study a class of binary treatment choice problems with partial identification through the lens of robust (multiple prior) Bayesian analysis.

• We use a convenient set of prior distributions to derive ex-ante and ex-post robust Bayes decision rules, both for decision makers who can randomize and for decision makers who cannot.
• Our main messages are as follows: First, ex-ante and ex-post robust Bayes decision rules do not agree in general, whether or not randomized rules are allowed.
• Second, randomized treatment assignment for some data realizations can be optimal in both ex-ante and, perhaps more surprisingly, ex-post problems.