Median Optimal Treatment Regimes
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AI 根据所提供的论文材料归类：摘要围绕处理规则、条件中位数处理效应及分布位置准则下的政策评价展开。
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中位数最优处理规则
- 研究问题
- 如何基于个体特征选择处理，使决策关注组内条件中位数，并减少均值准则受少数极端结果影响及边际中位数的跨组影响？
- 方法与思路
- 作者按条件中位数处理效应选择处理，定义政策价值 ACME，并构造具有双重稳健风格的政策价值及条件中位数效应估计量。
- 材料陈述的贡献
- 摘要称，该规则最大化 ACME，并给出其非参数效率界和达到效率界的估计量；同时提供模拟与 HIV 随机试验示例。
- 阅读关注点
- 应核对 ACME 的精确定义、条件与边际中位数最优化的区别，以及双重稳健性质、效率界和学习规则所需的条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收状态未经核验；摘要未列出所谓弱条件，也不能据此保证任意分布下的稳健性。
原文摘要
Optimal treatment regimes are personalized policies for making a treatment decision based on subject characteristics, with the policy chosen to maximize some value. It is common to aim to maximize the mean outcome in the population, via a regime assigning treatment only to those whose mean outcome is higher under treatment versus control. However, the mean can be an unstable measure of centrality, resulting in imprecise statistical procedures, as well as unrobust decisions that can be overly influenced by a small fraction of subjects. In this work, we propose a new median optimal treatment regime that instead treats individuals whose conditional median is higher under treatment. This ensures that optimal decisions for individuals from the same group are not overly influenced either by (i) a small fraction of the group (unlike the mean criterion), or (ii) unrelated subjects from different groups (unlike marginal median/quantile criteria). We introduce a new measure of value, the Average Conditional Median Effect (ACME), which summarizes across-group median treatment outcomes of a policy, and which the median optimal treatment regime maximizes. After developing key motivating examples that distinguish median optimal treatment regimes from mean and marginal median optimal treatment regimes, we give a nonparametric efficiency bound for estimating the ACME of a policy, and propose a new doubly robust-style estimator that achieves the efficiency bound under weak conditions. To construct the median optimal treatment regime, we introduce a new doubly robust-style estimator for the conditional median treatment effect. Finite-sample properties are explored via numerical simulations and the proposed algorithm is illustrated using data from a randomized clinical trial in patients with HIV.