对比两种因果识别代理变量方法的适用条件与假设。
Comparing Two Proxy Methods for Causal Identification
- 通过积分方程与特征分解两类思路识别因果效应。
- 揭示两类方法在模型假设上的差异与实际适用范围。
- 适合研究因果推断中未观测混杂因素的学者参考。
在存在未测量变量的情况下识别因果效应是因果推断中的基本挑战，代理变量方法为此提供了有力解决方案。本文对比了该框架下的两种主要方法：(1) 桥接方程法，利用积分方程求解来恢复因果目标；(2) 数组分解法，通过特征分解恢复潜在因子以识别反事实量。我们分析了这两种方法背后的模型限制，深入阐释其底层假设的影响，明确了每种方法的适用范围。
原文摘要 · Abstract (English)
Identifying causal effects in the presence of unmeasured variables is a fundamental challenge in causal inference, for which proxy variable methods have emerged as a powerful solution. We contrast two major approaches in this framework: (1) bridge equation methods, which leverage solutions to integral equations to recover causal targets, and (2) array decomposition methods, which recover latent factors used to identify counterfactual quantities via eigendecomposition tasks. We compare the model restrictions underlying these two approaches and provide insight into implications of the underlying assumptions, clarifying the scope of applicability for each method.
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