Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach
Hongming Pu and
Bo Zhang
Journal of the Royal Statistical Society Series B, 2021, vol. 83, issue 2, 318-345
Abstract:
Individualized treatment rules (ITRs) are considered a promising recipe to deliver better policy interventions. One key ingredient in optimal ITR estimation problems is to estimate the average treatment effect conditional on a subject’s covariate information, which is often challenging in observational studies due to the universal concern of unmeasured confounding. Instrumental variables (IVs) are widely used tools to infer the treatment effect when there is unmeasured confounding between the treatment and outcome. In this work, we propose a general framework of approaching the optimal ITR estimation problem when a valid IV is allowed to only partially identify the treatment effect. We introduce a novel notion of optimality called ‘IV‐optimality’. A treatment rule is said to be IV‐optimal if it minimizes the maximum risk with respect to the putative IV and the set of IV identification assumptions. We derive a bound on the risk of an IV‐optimal rule that illuminates when an IV‐optimal rule has favourable generalization performance. We propose a classification‐based statistical learning method that estimates such an IV‐optimal rule, design computationally efficient algorithms, and prove theoretical guarantees. We contrast our proposed method to the popular outcome weighted learning (OWL) approach via extensive simulations, and apply our method to study which mothers would benefit from travelling to deliver their premature babies at hospitals with high‐level neonatal intensive care units. R package ivitr implements the proposed method.
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)
Downloads: (external link)
https://doi.org/10.1111/rssb.12413
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:bla:jorssb:v:83:y:2021:i:2:p:318-345
Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868
Access Statistics for this article
Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom
More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().