José Luis Montiel Olea, Chen Qiu, Jörg Stoye
arXiv 29 Dec 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2312.17623 · PDF · DOI · OpenAlex · Extracted main text
We apply classical statistical decision theory to a large class of treatment choice problems with partial identification. We show that, in a general class of problems with Gaussian likelihood, all decision rules are admissible; it is maximin-welfare optimal to ignore all data; and, for severe enough partial identification, there are infinitely many minimax-regret optimal decision rules, all of which sometimes randomize the policy recommendation. We uniquely characterize the minimax-regret optimal rule that least frequently randomizes, and show that, in some cases, it can outperform other minimax-regret optimal rules in terms of what we term profiled regret. We analyze the implications of our results in the aggregation of experimental estimates for policy adoption, extrapolation of Local Average Treatment Effects, and policy making in the presence of omitted variable bias.
appendix boundary found by appendix_command · 37% 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 | Ishihara, T. and T. Kitagawa (2021) Evidence Aggregation for Treatment Choice, ArXiv:2108.06473 [econ.EM], https://doi.org/10.48550/arXiv.2108.06473 | 1.000 | 16 | 3 | 100% |
| 2 | Christensen, T., H. R. Moon, and F. Schorfheide (2022) Optimal Discrete Decisions when Payoffs are Partially Identified | 1.000 | 5 | 3 | 100% |
| 3 | Tetenov, A (2012) Statistical treatment choice based on asymmetric minimax regret criteria | 0.928 | 4 | 3 | 100% |
| 4 | Yata, K (2023) Optimal Decision Rules Under Partial Identification, ArXiv:2111.04926 [econ.EM], https://doi.org/10.48550/arXiv.2111.04926 | 0.874 | 13 | 2 | 100% |
| 5 | Diegert, P., M. A. Masten, and A. Poirier (2022) Assessing omitted variable bias when the controls are endogenous | 0.874 | 7 | 2 | 100% |
| 6 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 0.874 | 6 | 2 | 100% |
| 7 | Stoye, J (2012) a): Minimax regret treatment choice with covariates or with limited validity of experiments self | 0.843 | 10 | 5 | 60% |
| 8 | Hirano, K. and J. R. Porter (2009) Asymptotics for statistical treatment rules | 0.811 | 4 | 2 | 100% |
| 9 | Wald, A (1950) Statistical Decision Functions | 0.811 | 4 | 2 | 100% |
| 10 | Mogstad, M. and A. Torgovitsky (2018) Identification and extrapolation of causal effects with instrumental variables | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 77 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.