One of the fundamental challenges in causal inference is to estimate the causal effect of a treatment on its outcome of interest from observational data. However, causal effect estimation often suffers from the impacts of confounding bias caused by unmeasured confounders that affect both the treatment and the outcome. The instrumental variable (IV) approach is a powerful way to eliminate the confounding bias from latent confounders. However, the existing IV-based estimators require a nominated IV, and for a conditional IV (CIV) the corresponding conditioning set too, for causal effect estimation. This limits the application of IV-based estimators. In this paper, by leveraging the advantage of disentangled representation learning, we propose a novel method, named DVAE.CIV, for learning and disentangling the representations of CIV and the representations of its conditioning set for causal effect estimations from data with latent confounders. Extensive experimental results on both synthetic and real-world datasets demonstrate the superiority of the proposed DVAE.CIV method against the existing causal effect estimators.

翻译：因果推断中的基本挑战之一是从观测数据中估计干预对结果的因果效应。然而，因果效应估计常常受到由未测量混杂因素（同时影响干预和结果）引起的混杂偏差的影响。工具变量方法是消除潜在混杂因素导致的混杂偏差的有效手段。然而，现有基于工具变量的估计方法需要指定一个工具变量，对于条件工具变量，还需指定其相应的条件集，才能进行因果效应估计。这限制了基于工具变量的估计方法的应用范围。本文通过利用解耦表示学习的优势，提出了一种名为DVAE.CIV的新方法，用于从含有潜在混杂因素的数据中学习和解耦条件工具变量的表示及其条件集的表示，从而实现因果效应估计。在合成数据集和真实世界数据集上的大量实验结果表明，所提出的DVAE.CIV方法相较于现有因果效应估计方法具有优越性。