Proximal Causal Inference for Marginal Counterfactual Survival Curves
边际反事实生存曲线的近端因果推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究未测量混杂下反事实生存曲线的识别及近端因果估计，因此最直接对应因果推断方向。
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边际反事实生存曲线的近端因果推断
- 研究问题
- 在右删失生存结局的观察研究中，存在未测量混杂时，如何利用观测代理变量识别并估计处理间的反事实生存差异？
- 方法与思路
- 作者扩展近端因果推断，借助混杂代理及处理混杂桥函数，提出近端逆概率加权和近端双重稳健估计量。
- 材料陈述的贡献
- 摘要称，建立生存对比的非参数识别，证明两类估计量的一致收敛及渐近正态性，并以模拟和右心导管研究展示方法。
- 阅读关注点
- 应核对代理变量与桥函数的识别条件、右删失的处理方式，以及双重稳健成立时允许哪些模型发生误设。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未获核实；摘要未完整说明代理、桥函数和删失条件，不能视为无条件消除混杂。
原文摘要
Contrasting marginal counterfactual survival curves across treatment arms is an effective and popular approach for inferring the causal effect of an intervention on a right-censored time-to-event outcome. A key challenge to drawing such inferences in observational settings is the possible existence of unmeasured confounding, which may invalidate most commonly used methods that assume no hidden confounding bias. In this paper, rather than making the standard no unmeasured confounding assumption, we extend the recently proposed proximal causal inference framework of Miao et al. (2018), Tchetgen et al. (2020), Cui et al. (2020) to obtain nonparametric identification of a causal survival contrast by leveraging observed covariates as imperfect proxies of unmeasured confounders. Specifically, we develop a proximal inverse probability-weighted (PIPW) estimator, the proximal analog of standard IPW, which allows the observed data distribution for the time-to-event outcome to remain completely unrestricted. PIPW estimation relies on a parametric model for a so-called treatment confounding bridge function relating the treatment process to confounding proxies. As a result, PIPW might be sensitive to model misspecification. To improve robustness and efficiency, we also propose a proximal doubly robust estimator and establish uniform consistency and asymptotic normality of both estimators. We conduct extensive simulations to examine the finite sample performance of our estimators, and proposed methods are applied to a study evaluating the effectiveness of right heart catheterization in the intensive care unit of critically ill patients.