Proximal Causal Inference for Complex Longitudinal Studies
复杂纵向研究中的近端因果推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要围绕未观测混杂下时变处理联合效应的识别、估计与效率展开。
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复杂纵向研究中的近端因果推断
- 研究问题
- 当时变协变量只是未观测混杂的有误差代理、序贯随机化不成立时，能否识别和估计时变处理的联合因果效应？
- 方法与思路
- 作者将近端因果推断扩展至纵向半参数边际结构均值模型，使用一对时变代理变量，构造正则渐近线性及双重稳健估计。
- 材料陈述的贡献
- 摘要称建立了非参数识别、正则渐近线性估计量的刻画和半参数效率界，并通过模拟与数据应用展示有限样本表现。
- 阅读关注点
- 应核对两类代理的条件、识别方程及效率界推导，明确双重稳健性对应哪些模型组合，以及纵向估计的实际实施步骤。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未列出代理识别条件，不能理解为任意不完美协变量都能消除混杂。
原文摘要
A standard assumption for causal inference about the joint effects of time-varying treatment is that one has measured sufficient covariates to ensure that within covariate strata, subjects are exchangeable across observed treatment values, also known as "sequential randomization assumption (SRA)". SRA is often criticized as it requires one to accurately measure all confounders. Realistically, measured covariates can rarely capture all confounders with certainty. Often covariate measurements are at best proxies of confounders, thus invalidating inferences under SRA. In this paper, we extend the proximal causal inference (PCI) framework of Miao et al. (2018) to the longitudinal setting under a semiparametric marginal structural mean model (MSMM). PCI offers an opportunity to learn about joint causal effects in settings where SRA based on measured time-varying covariates fails, by formally accounting for the covariate measurements as imperfect proxies of underlying confounding mechanisms. We establish nonparametric identification with a pair of time-varying proxies and provide a corresponding characterization of regular and asymptotically linear estimators of the parameter indexing the MSMM, including a rich class of doubly robust estimators, and establish the corresponding semiparametric efficiency bound for the MSMM. Extensive simulation studies and a data application illustrate the finite sample behavior of proposed methods.