ROBUST BAYES TREATMENT CHOICE WITH PARTIAL IDENTIFICATION
研究了部分识别下二元干预选择问题，用稳健贝叶斯方法导出事前和事后最优规则，发现二者未必一致，随机化规则可能有用，并将结果应用于政策外部有效性和多实验数据汇总。
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. Therefore, it is usually with loss of generality to exclude randomized rules from consideration, even when regret is evaluated ex post. We apply our results to a stylized problem where a policy maker uses experimental data to choose whether to implement a new policy in a population of interest, but is concerned about the external validity of the experiment at hand, and to aggregation of data generated by multiple randomized control trials in different sites to make a policy choice in a population for which no experimental data are available.