Takuya Ishihara, Toru Kitagawa
arXiv 14 Aug 2021 · Econometrics · 5 citations (OpenAlex)
arXiv:2108.06473 · PDF · DOI · OpenAlex · Extracted main text
Consider a planner who has limited knowledge of the policy's causal impact on a certain local population of interest due to a lack of data, but does have access to the publicized intervention studies performed for similar policies on different populations. How should the planner make use of and aggregate this existing evidence to make her policy decision? Following Manski (2020; Towards Credible Patient-Centered Meta-Analysis, Epidemiology), we formulate the planner's problem as a statistical decision problem with a social welfare objective, and solve for an optimal aggregation rule under the minimax-regret criterion. We investigate the analytical properties, computational feasibility, and welfare regret performance of this rule. We apply the minimax regret decision rule to two settings: whether to enact an active labor market policy based on 14 randomized control trial studies; and whether to approve a drug (Remdesivir) for COVID-19 treatment using a meta-database of clinical trials.
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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 | Stoye, J (2012) Minimax regret treatment choice with covariates or with limited validity of experiments | 1.000 | 13 | 3 | 100% |
| 2 | Manski, C. F (2020) Towards credible patient-centered meta-analysis | 1.000 | 5 | 3 | 100% |
| 3 | Yata, K (2023) Optimal decision rules under partial identification | 1.000 | 5 | 3 | 100% |
| 4 | Montiel Olea, J., C. Qiu, and J. Stoye (2023) Decision theory for treatment choice problems with partial identification | 0.928 | 4 | 3 | 100% |
| 5 | Tetenov, A (2012) Statistical treatment choice based on asymmetric minimax regret criteria | 0.928 | 4 | 3 | 100% |
| 6 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 0.811 | 4 | 2 | 100% |
| 7 | Card, D., J. Kluve, and A. Weber (2017) What works? A meta analysis of recent active labor market program evaluations | 0.737 | 3 | 2 | 100% |
| 8 | Juul, S., E. E. Nielsen, J. Feinberg, F. Siddiqui, C. K. Jrgensen, E… (2020) Interventions for treatment of COVID-19: A living systematic review with meta-analyses and trial sequential analyses (The LIVING… | 0.737 | 3 | 2 | 100% |
| 9 | Stoye, J (2009) Minimax regret treatment choice with finite samples | 0.737 | 3 | 2 | 100% |
| 10 | Pan, H., F. Richard Peto, Q. A. Karim, M. Marissa Alejandria, A. M.… (2021) Repurposed antiviral drugs for COVID-19—interim WHO SOLIDARITY trial results | 0.644 | 4 | 1 | 100% |
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