Bayesian principal stratification with longitudinal data and truncation by death
纵向数据与死亡截断下的贝叶斯主分层AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确使用主分层处理死亡截断，并估计存活者平均因果效应。
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纵向数据与死亡截断下的贝叶斯主分层
- 研究问题
- 当个体在不同时间死亡、结局此后不再定义时，如何估计不同时间点始终存活主层的处理效应及其变化？
- 方法与思路
- 作者按潜在纵向死亡状态交叉划分主层，建立贝叶斯框架，分析主层构成、基线特征和存活者平均因果效应的时间趋势。
- 材料陈述的贡献
- 摘要称将单一时点的主分层扩展到纵向死亡截断情形，并以促进创业政策对企业存活和招聘的观察性研究说明方法。
- 阅读关注点
- 应核对跨时间主层的定义与约束、主层效应的识别及先验敏感性，以及应用中的处理分配强可忽略性如何论证。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明主层识别的全部限制、先验设定和应用效应大小。
原文摘要
In many causal studies, outcomes are censored by death, in the sense that they are neither observed nor defined for units who die. In such studies, the focus is usually on the stratum of always survivors up to a single fixed time s. Building on a recent strand of the literature, we propose an extended framework for the analysis of longitudinal studies, where units can die at different time points, and the main endpoints are observed and well defined only up to the death time. We develop a Bayesian longitudinal principal stratification framework, where units are cross classified according to the longitudinal death status. Under this framework, the focus is on causal effects for the principal strata of units that would be alive up to a time point s irrespective of their treatment assignment, where these strata may vary as a function of s. We can get precious insights into the effects of treatment by inspecting the distribution of baseline characteristics within each longitudinal principal stratum, and by investigating the time trend of both principal stratum membership and survivor-average causal effects. We illustrate our approach for the analysis of a longitudinal observational study aimed to assess, under the assumption of strong ignorability of treatment assignment, the causal effects of a policy promoting start ups on firms survival and hiring policy, where firms hiring status is censored by death.