论文标题
从观察数据中发现因果推断的祖先仪器变量
Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data
论文作者
论文摘要
仪器变量（IV）是一种有力的方法，即使治疗和结果之间存在潜在的混杂因素，也可以推断出治疗对观察数据结果的因果影响。但是，现有的IV方法要求选择iV，并通过域知识为合理。无效的IV可能导致估计值。因此，发现有效的IV对于IV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算法，以在轻度假设下从数据中发现有效的IV。我们基于部分祖先图（PAG）来开发理论，以支持搜索一组候选祖先IVS（AIV），并为每个可能的AIV识别其条件设置。基于该理论，我们提出了一种数据驱动算法，以从数据中发现一对IV。关于合成和现实世界数据集的实验表明，与基于最新的IV的因果关系效应估计量相比，开发的IV发现算法估计因果效应的准确估计。
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 paper, 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.
