A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity

Zhihao Zhao and Congyang Zhou ()
Additional contact information
Zhihao Zhao: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Congyang Zhou: School of Statistics, Capital University of Economics and Business, Beijing 100070, China

Mathematics, 2025, vol. 13, issue 11, 1-25

Abstract: Estimating heterogeneous treatment effects plays a vital role in many statistical applications, such as precision medicine and precision marketing. In this paper, we propose a novel meta-learner, termed RXlearner for estimating the conditional average treatment effect (CATE) within the general framework of meta-algorithms. RXlearner enhances the weighting mechanism of the traditional Xlearner to improve estimation accuracy. We establish non-asymptotic error bounds for RXlearner under a continuity classification criterion, specifically assuming that the response function satisfies Hölder continuity. Moreover, we show that these bounds are achievable by selecting an appropriate base learner. The effectiveness of the proposed method is validated through extensive simulation studies and a real-world data experiment.

Keywords: conditional average treatment effect; heterogeneous treatment effect; causal inference; minimax optimality; Hölder continuous (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/11/1739/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/11/1739/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:11:p:1739-:d:1663635

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().