论文标题
与异质观察数据的因果发现
Causal Discovery with Heterogeneous Observational Data
论文作者
论文摘要
我们考虑从异质观察数据中考虑因果发现（结构学习）的问题。大多数现有方法都采用均匀的抽样方案，这在许多应用中违反时会导致误导性结论。为此，我们提出了一种新颖的方法，该方法利用数据异质性从因果关系不足来推断可能的循环因果结构。核心思想是将直接因果效应建模为适当解释数据异质性的外源协变量的功能。我们研究了所提出的模型的结构可识别性。结构学习以完全贝叶斯的方式进行，这提供了自然的不确定性量化。我们通过广泛的模拟和现实应用程序来证明其实用性。
We consider the problem of causal discovery (structure learning) from heterogeneous observational data. Most existing methods assume a homogeneous sampling scheme, which leads to misleading conclusions when violated in many applications. To this end, we propose a novel approach that exploits data heterogeneity to infer possibly cyclic causal structures from causally insufficient systems. The core idea is to model the direct causal effects as functions of exogenous covariates that properly explain data heterogeneity. We investigate structure identifiability properties of the proposed model. Structure learning is carried out in a fully Bayesian fashion, which provides natural uncertainty quantification. We demonstrate its utility through extensive simulations and a real-world application.
