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Evidence Aggregation for Treatment Choice

Takuya Ishihara, Toru Kitagawa

arXiv 14 Aug 2021 · Econometrics · 5 citations (OpenAlex)

arXiv:2108.06473 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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.

Citation extraction

78
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distinct cited
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appendix boundary found by appendix_titled_section at “Appendix 1: Proofs” · 94% of the source is main text. Read the extracted text to check this.

Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Stoye, J (2012) Minimax regret treatment choice with covariates or with limited validity of experiments1.000133100%
2Manski, C. F (2020) Towards credible patient-centered meta-analysis1.00053100%
3Yata, K (2023) Optimal decision rules under partial identification1.00053100%
4Montiel Olea, J., C. Qiu, and J. Stoye (2023) Decision theory for treatment choice problems with partial identification0.92843100%
5Tetenov, A (2012) Statistical treatment choice based on asymmetric minimax regret criteria0.92843100%
6Manski, C. F (2004) Statistical treatment rules for heterogeneous populations0.81142100%
7Card, D., J. Kluve, and A. Weber (2017) What works? A meta analysis of recent active labor market program evaluations0.73732100%
8Juul, 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…0.73732100%
9Stoye, J (2009) Minimax regret treatment choice with finite samples0.73732100%
10Pan, H., F. Richard Peto, Q. A. Karim, M. Marissa Alejandria, A. M.… (2021) Repurposed antiviral drugs for COVID-19—interim WHO SOLIDARITY trial results0.64441100%

Showing the top 10 of 78 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Optimal Decision Rules Under Partial Identification1.000145
2Robust Bayes Treatment Choice with Partial Identification1.00084
3Bandwidth Selection for Treatment Choice with Binary Outcomes0.969116
4Shrinkage Methods for Treatment Choice0.84353
5Optimal Decision Rules when Payoffs are Partially Identified0.81142
6Locally Asymptotically Minimax Statistical Treatment Rules Under Partial Identification0.51121
7Statistical Decisions and Partial Identification: With Application to Boundary Discontinuity Design0.51121
8Orthogonal Policy Learning Under Ambiguity0.40511
9Robust Estimation and Inference in Panels with Interactive Fixed Effects0.40511
10Stochastic treatment choice with empirical welfare updating0.40511