Variable selection for individualised treatment rules with discrete outcomes
针对观察性研究中个体化治疗规则的变量选择问题，提出一种双重稳健的变量选择方法，能有效剔除无关变量，提升治疗规则的效率与可实施性。
An individualised treatment rule (ITR) is a decision rule that aims to improve individuals' health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many variables that are irrelevant to treatment decisions. Including all variables in an ITR could yield low efficiency and a complicated treatment rule that is difficult to implement. Thus, selecting variables to improve the treatment rule is crucial. We propose a doubly robust variable selection method for ITRs, and show that it compares favourably with competing approaches. We illustrate the proposed method on data from an adaptive, web-based stress management tool.