Adaptive Proximal Causal Inference with Some Invalid Proxies
含部分无效代理变量的自适应近端因果推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确涉及隐藏混杂下的因果效应识别，并在近端线性结构方程模型中构造估计量。
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含部分无效代理变量的自适应近端因果推断
- 研究问题
- 存在隐藏混杂且多个代理变量可能违反排除限制时，哪些条件仍能识别因果效应，如何同时筛选代理并进行推断？
- 方法与思路
- 作者提出识别的必要充分条件，在近端线性结构方程模型中构造 LASSO 中位数估计，并以差异惩罚发展自适应方法。
- 材料陈述的贡献
- 摘要称存在有效结果代理时，自适应估计具有根号样本量一致性和有效置信区间，并扩展至多个结果代理可能无效的情形。
- 阅读关注点
- 应核对必要充分条件的精确内容、有效代理的数量与配置要求，以及自适应惩罚、选择一致性和区间有效性的条件。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未列出识别条件，理论保证不能直接推广至任意模型。
原文摘要
Proximal causal inference (PCI) is a recently proposed framework to identify and estimate the causal effect of an exposure on an outcome in the presence of hidden confounders, using observed proxies. Specifically, PCI relies on two types of proxies: a treatment-inducing confounding proxy, related to the outcome only through its association with unmeasured confounders (given treatment and covariates), and an outcome-inducing confounding proxy, related to the treatment only through such association (given covariates). These proxies must satisfy stringent exclusion restrictions - namely, the treatment proxy must not affect the outcome, and the outcome proxy must not be affected by the treatment. To improve identification and potentially efficiency, multiple proxies are often used, raising concerns about bias from exclusion violations. To address this, we introduce necessary and sufficient conditions for identifying causal effects in the presence of many proxies, some potentially invalid. Under a canonical proximal linear structural equations model, we propose a LASSO-based median estimator that jointly selects valid proxies and estimates the causal effect, with theoretical guarantees. Recognizing LASSO's limitations in consistently selecting valid treatment proxies, we develop an adaptive LASSO-based estimator with differential penalization. We show that it is root-n consistent and yields valid confidence intervals when a valid outcome proxy is available. We also extend the approach to settings with many potentially invalid outcome proxies. Theoretical results are supported by simulations and an application assessing the effect of right heart catheterization on 30-day survival in ICU patient.