Targeted Learning on Variable Importance Measure for Heterogeneous Treatment Effect
异质性处理效应变量重要性的目标学习AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究条件平均处理效应的异质性及其变量重要性参数。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
异质性处理效应变量重要性的目标学习
- 研究问题
- 如何量化特定协变量集合对处理效应异质性的解释程度，并为这种变量重要性参数构造偏差较小、区间覆盖较好的估计？
- 方法与思路
- 作者将重要性定义为条件平均处理效应方差之差，提出先估计相关分量、再迭代更新的两步目标最大似然纯代入估计量。
- 材料陈述的贡献
- 摘要称模拟中该估计量相较简单代入和估计方程方法具有较低偏差、较好覆盖；HIV 随机试验应用得到相近的重要性排序。
- 阅读关注点
- 应核对重要性参数的精确定义、目标更新与推断条件，以及纯代入估计怎样满足分布约束；同时检查模拟中的比较设定。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明识别与渐近推断条件，变量重要性排序也不能直接解释为变量的因果效应。
原文摘要
Quantifying the heterogeneity of treatment effect is important for understanding how a commercial product or medical treatment affects different population subgroups. While much of treatment effect heterogeneity analysis focuses on the conditional average treatment effect, an alternative parameter that captures treatment effect heterogeneity is the variance of treatment effect across different covariate groups. One can also derive variable importance parameters that measure (and rank) how much of treatment effect heterogeneity is explained by a targeted subset of covariates. In this article, we propose a new targeted maximum likelihood estimator (TMLE) for a treatment effect variable importance measure, in the form of the difference of the variances of conditional average treatment effect. This TMLE is a pure plug-in estimator that consists of two steps: 1) the initial estimation of relevant components to plug in and 2) an iterative updating step to optimize the bias-variance tradeoff. Simulation results show that the proposed estimator has competitive performance in terms of lower bias and better confidence interval coverage compared to a simple substitution estimator and an estimating equation estimator. We apply these methods to data from a randomized clinical trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults. We find that the estimating equation estimator and the proposed TMLE suggest similar rankings of variable importance. The application of this method also demonstrates the advantage of a plug-in estimator, which always respects the global constraints on the data distribution and that the estimand is a particular function of the distribution.