Evidence Aggregation for Treatment Choice

Takuya Ishihara; Toru Kitagawa

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arXiv · 2108.06473

Evidence Aggregation for Treatment Choice

Takuya Ishihara·Toru Kitagawa

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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, \textit{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 decide whether to enact an active labor market policy based on 14 randomized control trial studies.

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Takuya Ishihara, Toru Kitagawa. 2021-08-14. Evidence Aggregation for Treatment Choice. https://arxiv.org/abs/2108.06473

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