Qiu Hongxiang, Carone Marco, Luedtke Alex
Department of Statistics, the Wharton School, University of Pennsylvania.
Department of Biostatistics, University of Washington.
J Causal Inference. 2022 Jan;10(1):480-493. doi: 10.1515/jci-2022-0005. Epub 2022 Dec 31.
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.
个性化治疗规则的估计和评估已得到广泛研究，但现实世界中的治疗资源限制在现有方法中受到的关注有限。我们研究了一种情况，即当治疗成本是随机的时，基于协变量对治疗进行干预，以在治疗成本约束下优化平均反事实结果。在一个特别有趣的特殊情况下，对应于鼓励治疗的工具变量会在接受治疗的比例受到限制的情况下进行干预。对于这种情况，我们首先开发一种方法来估计最优个性化治疗规则。我们进一步构建了一个相对于给定参考规则的相应平均治疗效果的渐近有效插件估计器。
