Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.spl.2021.109180
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dc.titleOn a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable
dc.contributor.authorCui, Yifan
dc.contributor.authorTchetgen Tchetgen, E.
dc.date.accessioned2022-10-13T01:17:46Z
dc.date.available2022-10-13T01:17:46Z
dc.date.issued2021-11-01
dc.identifier.citationCui, Yifan, Tchetgen Tchetgen, E. (2021-11-01). On a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable. Statistics and Probability Letters 178 : 109180. ScholarBank@NUS Repository. https://doi.org/10.1016/j.spl.2021.109180
dc.identifier.issn0167-7152
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232898
dc.description.abstractUnmeasured confounding is a threat to causal inference and individualized decision making. Similar to Cui and Tchetgen Tchetgen (2021); Qiu et al. (2021); Han (2021), we consider the problem of identification of optimal individualized treatment regimes with a valid instrumental variable. Han (2021) provided an alternative identifying condition of optimal treatment regimes using the conditional Wald estimand of Cui and Tchetgen Tchetgen (2021); Qiu et al. (2021) when treatment assignment is subject to endogeneity and a valid binary instrumental variable is available. In this note, we provide a necessary and sufficient condition for identification of optimal treatment regimes using the conditional Wald estimand. Our novel condition is necessarily implied by those of Cui and Tchetgen Tchetgen (2021); Qiu et al. (2021); Han (2021) and may continue to hold in a variety of potential settings not covered by prior results. © 2021
dc.publisherElsevier B.V.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceScopus OA2021
dc.subjectConditional average treatment effect
dc.subjectIndividualized decision making
dc.subjectOptimal treatment regimes
dc.subjectPolicy making
dc.subjectSign identification
dc.subjectUnmeasured confounding
dc.typeArticle
dc.contributor.departmentSTATISTICS AND DATA SCIENCE
dc.description.doi10.1016/j.spl.2021.109180
dc.description.sourcetitleStatistics and Probability Letters
dc.description.volume178
dc.description.page109180
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