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.

本文提出一种基于部分祖先图的数据驱动算法用于发现有效的工具变量，并在合成数据和真实世界数据集上进行实验证明其对因果效应估计的准确性优于现有基于工具变量的因果效应估计方法。