仅用一个代理变量即可识别因果效应，突破传统方法依赖多个观测的限制。
Identifying Causal Effects Using a Single Proxy Variable
- 基于单个代理变量与未观测混杂因子的生成机制，提出可识别性理论
- 在高维、非线性关系和广义分布下仍能准确估计因果效应
- 开发神经网络框架SPICE-Net，适用于离散与连续处理变量
在科学应用中，未观测混杂是估计处理对结果因果效应的关键挑战。本文假设可观测到一个可能为多维的未观测混杂因子的代理变量，并且已知该代理变量从混杂因子生成的机制。在我们称之为单代理因果效应可识别性（SPICE）的完备性假设下，证明了因果效应可被识别。本工作将Kuroki与Pearl（2014）、Pearl（2010）的代理变量因果可识别性结果拓展至更高维度、更灵活的函数关系以及更广泛的分布类别。此外，我们提出了基于神经网络的估计框架SPICE-Net，可用于离散和连续处理变量的因果效应估计。
原文摘要 · Abstract (English)
Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome in scientific applications. In this work, we assume that we observe a single, potentially multi-dimensional proxy variable of the unobserved confounder and that we know the mechanism that generates the proxy from the confounder. Under a completeness assumption on this mechanism, which we call Single Proxy Identifiability of Causal Effects or simply SPICE, we prove that causal effects are identifiable. We extend the proxy-based causal identifiability results by Kuroki and Pearl (2014); Pearl (2010) to higher dimensions, more flexible functional relationships and a broader class of distributions. Further, we develop a neural network based estimation framework, SPICE-Net, to estimate causal effects, which is applicable to both discrete and continuous treatments.
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