Policy Learning withα -Expected Welfare

Yanqin Fan; Yuan Qi; Gaoqian Xu

doi:10.48550/arxiv.2505.00256

Back

Preprint

Open access

Policy Learning withα -Expected Welfare

Yanqin Fan, Yuan Qi and Gaoqian Xu

arXiv

04/30/2025

DOI: 10.48550/arxiv.2505.00256

Files and links (1)

url

https://doi.org/10.48550/arxiv.2505.00256View

Preprint (Author's original) This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

This paper proposes an optimal policy that targets the average welfare of the worst-off α-fraction of the post-treatment outcome distribution. We refer to this policy as the α-Expected Welfare Maximization (α-EWM) rule, where α∈(0,1] denotes the size of the subpopulation of interest. The α-EWM rule interpolates between the expected welfare (α=1) and the Rawlsian welfare (α→0). For α∈(0,1), an α-EWM rule can be interpreted as a distributionally robust EWM rule that allows the target population to have a different distribution than the study population. Using the dual formulation of our α-expected welfare function, we propose a debiased estimator for the optimal policy and establish its asymptotic upper regret bounds. In addition, we develop asymptotically valid inference for the optimal welfare based on the proposed debiased estimator. We examine the finite sample performance of the debiased estimator and inference via both real and synthetic data.

Details

Title: Subtitle: Policy Learning withα -Expected Welfare
Creators: Yanqin Fan
Yuan Qi
Gaoqian Xu
Resource Type: Preprint
Publication Details: arXiv
DOI: 10.48550/arxiv.2505.00256
ISSN: 2331-8422
Publisher: arXiv
Language: English
Date posted: 04/30/2025
Academic Unit: Economics
Record Identifier: 9985217109702771

Metrics

1 Record Views