Individualized treatment rules under stochastic treatment cost constraints

Qiu Hongxiang (), Carone Marco () and Luedtke Alex ()
Additional contact information
Qiu Hongxiang: Department of Statistics, the Wharton School, University of Pennsylvania, Philadelphia, PA 19104, United States
Carone Marco: Department of Biostatistics, University of Washington, Seattle, Washington, United States
Luedtke Alex: Department of Statistics, University of Washington, Seattle, Washington, United States

Journal of Causal Inference, 2022, vol. 10, issue 1, 480-493

Abstract: Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods. We investigate a setting in which treatment is intervened upon based on covariates to optimize the mean counterfactual outcome under treatment cost constraints when the treatment cost is random. In a particularly interesting special case, an instrumental variable corresponding to encouragement to treatment is intervened upon with constraints on the proportion receiving treatment. For such settings, we first develop a method to estimate optimal individualized treatment rules. We further construct an asymptotically efficient plug-in estimator of the corresponding average treatment effect relative to a given reference rule.

Keywords: nonparametric inference; average treatment effect; dynamic treatment regime (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1515/jci-2022-0005 (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:10:y:2022:i:1:p:480-493:n:1

DOI: 10.1515/jci-2022-0005

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 ().