Causal Inference with Categorical Unobserved Confounder via Mixture Learning
通过混合分布学习处理分类未观测混杂的因果推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确以未观测混杂下的因果效应识别为目标，并讨论代理变量、多重处理及潜在混杂恢复。
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通过混合分布学习处理分类未观测混杂的因果推断
- 研究问题
- 未观测混杂变量为分类变量时，能否在代理变量或多重处理场景中恢复混杂结构并识别因果效应，减少对代理变量非对称角色的依赖？
- 方法与思路
- 作者将潜在混杂结构转化为混合分布识别问题，提出基于张量分解的估计程序，并研究潜在结构的一致恢复及非渐近保证。
- 材料陈述的贡献
- 摘要称在适当条件下，两类场景中的因果效应均可识别，所提方法具有一致性和非渐近保证；模拟与真实数据实验展示有限数据下的表现。
- 阅读关注点
- 应核对分类混杂的类别数、代理或多重处理所需独立性、混合分布识别条件，以及张量分解的稳定性与因果效应恢复步骤。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出适当条件或误差界，不能据此认定分类混杂本身足以解决所有未观测混杂问题。
原文摘要
Unobserved confounding is a fundamental challenge for estimating causal effects. To address unobserved confounding, recent literature has turned to two different approaches -- proxy variables and the use of multiple treatments. The first approach, commonly referred to as proximal causal inference, requires proxies to be assigned to specific asymmetric roles: treatment-inducing proxies (negative control exposures), variables that act as common causes of the treatment and outcome, and outcome-inducing proxies (negative control outcomes). In practice, however, identifying variables that satisfy these asymmetric roles can be difficult depending on the application domain. The second approach, commonly referred to as the ``Deconfounder," deals with multiple conditionally independent treatments. There has been limited progress towards developing a consistent estimation method for this setting. As the primary contribution of this work, we establish that causal effects are identifiable in both settings when the unobserved confounder is categorical under suitable conditions. Our approach builds on a mixture learning perspective: we show that the underlying confounding structure can be recovered by identifying the corresponding mixture distribution. We propose an estimation procedure based on tensor decomposition, which allows consistent recovery of the latent structure and comes with non-asymptotic guarantees. Simulation studies and real data experiments demonstrate that the proposed method performs well even with limited data.