CoChat
Sign Up
Cited by 277Advanced Causal Inference TechniquesEvaluation and Performance AssessmentGame Theory and Voting Systems

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice

Open access
Kitagawa, Toru, Tetenov, Aleksey·2018·Econometrica
DOI 10.3982/ecta13288
Open this paper in CoChat — free accountOpen PDF

Abstract

One of the main objectives of empirical analysis of experiments and quasi-experiments is to inform policy decisions that determine the allocation of treatments to individuals with different observable covariates. We study the properties and implementation of the Empirical Welfare Maximization (EWM) method, which estimates a treatment assignment policy by maximizing the sample analog of average social welfare over a class of candidate treatment policies. The EWM approach is attractive in terms of both statistical performance and practical implementation in realistic settings of policy design. Common features of these settings include: (i) feasible treatment assignment rules are constrained exogenously for ethical, legislative, or political reasons, (ii) a policy maker wants a simple treatment assignment rule based on one or more eligibility scores in order to reduce the dimensionality of individual observable characteristics, and/or (iii) the proportion of individuals who can receive the treatment is a priori limited due to a budget or a capacity constraint. We show that when the propensity score is known, the average social welfare attained by EWM rules converges at least at n1/2 rate to the maximum obtainable welfare uniformly over a minimally constrained class of data distributions, and this uniform convergence rate is minimax optimal. We examine how the uniform convergence rate depends on the richness of the class of candidate decision rules, the distribution of conditional treatment effects, and the lack of knowledge of the propensity score. We offer easily implementable algorithms for computing the EWM rule and an application using experimental data from the National JTPA Study.

Overview

Integrity signals

Registry

Crossref · 10.3982/ecta13288

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The text we found for this paper looks like another document, so the self-check wasn’t run.

Citations

Cites

Flagged first, then by citations

Related

Metadata

Authors
Kitagawa, T., & Tetenov, A.
Affiliations
University of Bristol (GB)
ORCID
Venue
Econometrica
Publisher
The Econometric Society
Published
2018
Volume
86
Issue
2
Pages
591-616
ISSN
0012-9682 (ISSN-L)
DOI
10.3982/ecta13288
OpenAlex
W2793124255
Cited by
277
References
36
Citations by year
2026: 51 · 2025: 66 · 2024: 39 · 2023: 39 · 2022: 26 · 2021: 21 · 2020: 22 · 2019: 8 · 2018: 5
FWCI
11.62
Type
journal article
Language
en
Open access
Locations
  • Econometrica (publishedVersion)
  • Explore Bristol Research (publishedVersion, other-oa)
  • Bristol Research (University of Bristol) (acceptedVersion, other-oa)
Funders
Economic and Social Research Council (ES/P008909/1)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Social Sciences › Public Administration › Evaluation and Performance Assessment
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
Keywords
social welfare maximization, uniform convergence, minimax optimality, propensity score, capacity constraints, conditional treatment effects, treatment allocation, policy design

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review