Unmeasured confounding is a threat to causal inference and individualized decision making. Similar to Cui and Tchetgen Tchetgen (2020); Qiu et al. (2020); Han (2020a), we consider the problem of identification of optimal individualized treatment regimes with a valid instrumental variable. Han (2020a) provided an alternative identifying condition of optimal treatment regimes using the conditional Wald estimand of Cui and Tchetgen Tchetgen (2020); Qiu et al. (2020) 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 (2020); Qiu et al. (2020); Han (2020a) and may continue to hold in a variety of potential settings not covered by prior results.
翻译:与Cui和Tchetgen Tchetgen(2020年);Qiu等人(2020年);Han(2020年a)相似,我们考虑用有效的工具变量确定最佳个人化治疗制度的问题。 Han(2020年a)利用Cui和Tchetgen等人有条件的Wald estimand (2020年)和Tchetgen Tchetgen (2020年); Qiu等人(202020年),当治疗分配受制于内分性并具备一个有效的二元工具变量时,我们为利用条件的Wald est estemand(Wald estimand)确定最佳个人化治疗制度提供了必要和充分的条件,因为Cui和Tchetgen(2020年); Que等人(2020年); Han (2020年)等(2020年)的情况必然意味着我们的新情况,并可能继续维持在先前结果未涵盖的各种潜在环境中。