ROBUST BAYES TREATMENT CHOICE WITH PARTIAL IDENTIFICATION
- 1Massachusetts Institute of Technology
- 2Cornell University
- 3Stanford University
Abstract
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.
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BibTeX
@article{fernandez2026robust,
title = {{ROBUST BAYES TREATMENT CHOICE WITH PARTIAL IDENTIFICATION}},
author = {Andrés Aradillas Fernández and José Luis Montiel Olea and Chen Qiu and Jörg Stoye and Serdil Tinda},
journal = {Econometric Theory},
year = {2026},
month = jul,
doi = {10.1017/s0266466626100486},
url = {https://doi.org/10.1017/s0266466626100486},
note = {CC BY},
}
RIS
TY - JOUR
TI - ROBUST BAYES TREATMENT CHOICE WITH PARTIAL IDENTIFICATION
AU - Andrés Aradillas Fernández
AU - José Luis Montiel Olea
AU - Chen Qiu
AU - Jörg Stoye
AU - Serdil Tinda
PY - 2026
DA - 2026/07/23
JO - Econometric Theory
DO - 10.1017/s0266466626100486
UR - https://doi.org/10.1017/s0266466626100486
AB - 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.
SN - 0266-4666
SN - 1469-4360
ER -