Evidence Aggregation for Treatment Choice
read the original 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 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.
This paper has not been read by Pith yet.
Forward citations
Cited by 2 Pith papers
-
Nonparametric Bayesian Policy Learning
NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent...
-
Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data
Proposes a minimax-regret framework for learning generalizable CATE models from multisite data by minimizing worst-case regret over convex combinations of site-specific CATEs.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.