Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice
From MaRDI portal
Publication:4628441
DOI10.3982/ECTA13288zbMath1419.91280OpenAlexW2793124255MaRDI QIDQ4628441
Toru Kitagawa, Aleksey Tetenov
Publication date: 13 March 2019
Published in: Econometrica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3982/ecta13288
randomized experimentsprogram evaluationempirical risk minimizationheterogeneous treatment effectsrisk boundsindividualized treatment rules
Related Items (20)
Functional Sequential Treatment Allocation ⋮ Optimal allocations to heterogeneous agents with an application to stimulus checks ⋮ Best subset binary prediction ⋮ On Robustness of Individualized Decision Rules ⋮ Debiased machine learning of set-identified linear models ⋮ Fair Policy Targeting ⋮ Treatment recommendation with distributional targets ⋮ Probabilistic prediction for binary treatment choice: with focus on personalized medicine ⋮ Off-policy evaluation in partially observed Markov decision processes under sequential ignorability ⋮ Comment: Invariance and causal inference ⋮ Performance guarantees for policy learning ⋮ Experimental Evaluation of Individualized Treatment Rules ⋮ Doubly robust treatment effect estimation with missing attributes ⋮ More Efficient Policy Learning via Optimal Retargeting ⋮ Learning Optimal Distributionally Robust Individualized Treatment Rules ⋮ Exact computation of censored least absolute deviations estimator ⋮ Learning When-to-Treat Policies ⋮ Unnamed Item ⋮ Who should get vaccinated? Individualized allocation of vaccines over SIR network ⋮ Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing
This page was built for publication: Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice
