Double Variable Importance Matching to Estimate Distinct Causal Effects on Event Probability and Timing
双重变量重要性匹配：区分事件概率与发生时间的因果效应AI 解读 ↓
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双重变量重要性匹配：区分事件概率与发生时间的因果效应
- 研究问题
- 研究存在混杂、删失和永不发生事件的治愈人群时，如何分别估计处理对治愈概率及非治愈者事件发生时间的异质影响。
- 方法与思路
- 作者用混合治愈模型提取两类目标的变量重要性，构建不同加权距离和匹配组，再利用组内 Kaplan–Meier 估计推导处理效应。
- 材料陈述的贡献
- 摘要称提供估计一致性及等尺度约束下的距离最优性保证，并将误差分为删失、模型拟合和不可约噪声，开展模拟与真实数据分析。
- 阅读关注点
- 应核对治愈与非治愈目标的因果定义、删失及混杂条件、长期随访要求，以及匹配组估计如何支持文中个体层面的效应解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实；摘要未给出完整识别条件，不能仅凭匹配与生存曲线认定个体因果效应已被识别。
原文摘要
In many clinical contexts, estimating effects of treatment in time-to-event data is complicated not only by confounding, censoring, and heterogeneity, but also by the presence of a cured subpopulation in which the event of interest never occurs. In such settings, treatment may have distinct effects on (1) the probability of being cured and (2) the event timing among non-cured individuals. Standard survival analysis and causal inference methods typically do not separate cured from non-cured individuals, obscuring distinct treatment mechanisms on cure probability and event timing. To address these challenges, we propose a matching-based framework that constructs distinct match groups to estimate heterogeneous treatment effects (HTE) on cure probability and event timing, respectively. We use mixture cure models to identify feature importance for both estimands, which in turn informs weighted distance metrics for matching in high-dimensional spaces. Within matched groups, Kaplan-Meier estimators provide estimates of cure probability and expected time to event, from which individual-level treatment effects are derived. We provide theoretical guarantees for estimator consistency and distance metric optimality under an equal-scale constraint. We further decompose estimation error into contributions from censoring, model fitting, and irreducible noise. Simulations and real-world data analyses demonstrate that our approach delivers interpretable and robust HTE estimates in time-to-event settings.