2D score-based estimation of heterogeneous treatment effects

Ye Steven Siwei (), Chen Yanzhen () and Padilla Oscar Hernan Madrid ()
Additional contact information
Ye Steven Siwei: Department of Statistics, University of California, Los Angeles, USA
Chen Yanzhen: Department of ISOM, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Padilla Oscar Hernan Madrid: Department of Statistics, University of California, Los Angeles, USA

Journal of Causal Inference, 2023, vol. 11, issue 1, 25

Abstract: Statisticians show growing interest in estimating and analyzing heterogeneity in causal effects in observational studies. However, there usually exists a trade-off between accuracy and interpretability for developing a desirable estimator for treatment effects, especially in the case when there are a large number of features in estimation. To make efforts to address the issue, we propose a score-based framework for estimating the conditional average treatment effect (CATE) function in this article. The framework integrates two components: (i) leverage the joint use of propensity and prognostic scores in a matching algorithm to obtain a proxy of the heterogeneous treatment effects for each observation and (ii) utilize nonparametric regression trees to construct an estimator for the CATE function conditioning on the two scores. The method naturally stratifies treatment effects into subgroups over a 2d grid whose axis are the propensity and prognostic scores. We conduct benchmark experiments on multiple simulated data and demonstrate clear advantages of the proposed estimator over state-of-the-art methods. We also evaluate empirical performance in real-life settings, using two observational data from a clinical trial and a complex social survey, and interpret policy implications following the numerical results.

Keywords: observational data; subgroup treatment effects; regression tree; matching (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2022-0016 (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:bpj:causin:v:11:y:2023:i:1:p:25:n:1007

DOI: 10.1515/jci-2022-0016

Access Statistics for this article

Journal of Causal Inference is currently edited by Elias Bareinboim, Jin Tian and Iván Díaz

More articles in Journal of Causal Inference from De Gruyter
Bibliographic data for series maintained by Peter Golla ().