Cheng Debo, Li Jiuyong, Liu Lin, Yu Kui, Duy Le Thuc, Liu Jixue
IEEE Trans Neural Netw Learn Syst. 2024 Aug;35(8):11542-11552. doi: 10.1109/TNNLS.2023.3262848. Epub 2024 Aug 5.
Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data even when there exist latent confounders between the treatment and the outcome. However, existing IV methods require that an IV is selected and justified with domain knowledge. An invalid IV may lead to biased estimates. Hence, discovering a valid IV is critical to the applications of IV methods. In this article, we study and design a data-driven algorithm to discover valid IVs from data under mild assumptions. We develop the theory based on partial ancestral graphs (PAGs) to support the search for a set of candidate ancestral IVs (AIVs), and for each possible AIV, the identification of its conditioning set. Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data. The experiments on synthetic and real-world datasets show that the developed IV discovery algorithm estimates accurate estimates of causal effects in comparison with the state-of-the-art IV-based causal effect estimators.
工具变量（IV）是一种强大的方法，即使在处理因素和感兴趣的结果之间存在潜在混杂因素时，也能从观测数据中推断出处理因素对感兴趣结果的因果效应。然而，现有的工具变量方法需要根据领域知识选择并证明工具变量的合理性。无效的工具变量可能导致有偏差的估计。因此，发现有效的工具变量对于工具变量方法的应用至关重要。在本文中，我们研究并设计了一种数据驱动的算法，在温和假设下从数据中发现有效的工具变量。我们基于部分祖先图（PAG）发展理论，以支持寻找一组候选祖先工具变量（AIV），并为每个可能的AIV确定其条件集。基于该理论，我们提出一种数据驱动的算法，从数据中发现一对工具变量。在合成数据集和真实世界数据集上的实验表明，与基于工具变量的最先进因果效应估计器相比，所开发的工具变量发现算法能估计出准确的因果效应。
