Comment: Complex Causal Questions Require Careful Model Formulation: Discussion of Rubin on Experiments with "Censoring" Due to Death

Rejoinder to Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with "Censoring" Due to Death

Rejoinder on Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with ``Censoring'' Due to Death by D. B. Rubin [math.ST/0612783]

基于广义Marshall-Olkin模型的具有同时死亡时间的相依删失

Dependent censoring with simultaneous death times based on the Generalized Marshall-Olkin model

本文研究失效时间可能相等的相依删失问题，提出基于Marshall-Olkin型模型，研究相关生存Copula性质，引入边际分布与联合生存概率估计量并证明其渐近正态性，最后通过模拟与真实数据验证方法性能。

Quantifying and Detecting Individual Level `Always Survivor' Causal Effects Under `Truncation by Death' and Censoring Through Time

The analysis of causal effects when the outcome of interest is possibly truncated by death has a long history in statistics and causal inference. The survivor average causal effect is commonly identified with more assumptions than those guaranteed by the design of a randomized clinical trial or using sensitivity analysis. This paper demonstrates that individual level causal effects in the `always survivor' principal stratum can be identified with no stronger identification assumptions than randomization. We illustrate the practical utility of our methods using data from a clinical trial on pat…

Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with "Censoring" Due to Death

Causal inference is best understood using potential outcomes. This use is particularly important in more complex settings, that is, observational studies or randomized experiments with complications such as noncompliance. The topic of this lecture, the issue of estimating the causal effect of a treatment on a primary outcome that is ``censored'' by death, is another such complication. For example, suppose that we wish to estimate the effect of a new drug on Quality of Life (QOL) in a randomized experiment, where some of the patients die before the time designated for their QOL to be assessed. …

基于替代信息的删失结果因果推断

Surrogate-powered Causal Inference with Censored Outcomes

针对生存数据删失导致的信息丢失，提出利用治疗后疾病史的目标保持估计器，通过影响函数和增益恒等式提升因果效应估计效率，并验证于乳腺癌数据。

相依删失下依从者因果风险比的估计

Estimation of the complier causal hazard ratio under dependent censoring

本文研究相依删失下内生二元处理对持续期结果的因果效应，利用二元工具变量并通过 copula 建模删失依赖关系，提出基于加权最大似然的两步估计法以估计依从者因果风险比（CCHR）。