用代理变量估算跨领域的因果效应，解决隐藏混杂因素干扰问题。
Transferring Causal Effects using Proxies
- 通过代理变量建模隐藏混杂因子，实现跨域因果推断。
- 理论证明在连续变量下仍可识别因果效应，且估计具一致性。
- 适用于数据受限但有代理变量的场景，如用户行为分析。
我们研究多领域设置下的因果效应估计问题，其中感兴趣的因果效应受未观测混杂因子影响，并可能在不同领域间变化。假设存在隐藏混杂因子的代理变量，且所有变量为离散或分类变量。本文提出一种在目标领域中仅观测代理变量时估计因果效应的方法。在该条件下，我们证明了因果效应的可识别性（即使处理和响应变量为连续型）。引入两种估计技术，证明其一致性并推导置信区间。理论结果通过模拟实验和一个真实案例研究验证，该案例分析网站排名对消费者选择的因果影响。
原文摘要 · Abstract (English)
We consider the problem of estimating a causal effect in a multi-domain setting. The causal effect of interest is confounded by an unobserved confounder and can change between the different domains. We assume that we have access to a proxy of the hidden confounder and that all variables are discrete or categorical. We propose methodology to estimate the causal effect in the target domain, where we assume to observe only the proxy variable. Under these conditions, we prove identifiability (even when treatment and response variables are continuous). We introduce two estimation techniques, prove consistency, and derive confidence intervals. The theoretical results are supported by simulation studies and a real-world example studying the causal effect of website rankings on consumer choices.
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