Andrés Aradillas Fernández, José Luis Montiel Olea, Chen Qiu, Jörg Stoye, Serdil Tinda
arXiv 21 Aug 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2408.11621 · PDF · DOI · OpenAlex · Extracted main text
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 tend to 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 (Stoye, 2012); and to the 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 (Manski, 2020; Ishihara and Kitagawa, 2021).
appendix boundary found by appendix_command · 53% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Takuya Ishihara and Toru Kitagawa (2021) Evidence Aggregation for Treatment Choice | 1.000 | 8 | 4 | 100% |
| 2 | Stoye, Jörg (2012) Minimax regret treatment choice with covariates or with limited validity of experiments self | 1.000 | 7 | 4 | 100% |
| 3 | Kohei Yata (2021) Optimal Decision Rules Under Partial Identification | 1.000 | 7 | 4 | 100% |
| 4 | Giacomini, Raffaella and Kitagawa, Toru (2021) Robust Bayesian Inference for Set-Identified Models | 1.000 | 7 | 3 | 100% |
| 5 | Montiel Olea, José Luis and Qiu, Chen and Stoye, Jörg (2025) Decision Theory for Treatment Choice Problems with Partial Identification self | 0.935 | 22 | 5 | 82% |
| 6 | Christensen, Timothy and Moon, Hyungsik Roger and Schorfheide, Frank (2022) Optimal Discrete Decisions when Payoffs are Partially Identified | 0.874 | 6 | 2 | 100% |
| 7 | Giacomini, R. and T. Kitagawa and M. Read (2021) Robust Bayesian Analysis for Econometrics | 0.874 | 5 | 2 | 100% |
| 8 | Berger, J.O (1985) Statistical Decision Theory and Bayesian Analysis | 0.874 | 5 | 2 | 100% |
| 9 | Aradillas Fernández, Andrés and Blanchet, José and Montiel Olea, Jos… (2025) $ $-Minimax Solutions of Statistical Decision Problems self | 0.737 | 3 | 3 | 67% |
| 10 | Guggenberger, Patrik and Huang, Jiaqi (2025) On the numerical approximation of minimax regret rules via fictitious play | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 71 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.