Causal Structure Learning (CSL), also referred to as causal discovery, amounts to extracting causal relations among variables in data. CSL enables the estimation of causal effects from observational data alone, avoiding the need to perform real life experiments. Constraint-based CSL leverages conditional independence tests to perform causal discovery. We propose Shapley-PC, a novel method to improve constraint-based CSL algorithms by using Shapley values over the possible conditioning sets, to decide which variables are responsible for the observed conditional (in)dependences. We prove soundness, completeness and asymptotic consistency of Shapley-PC and run a simulation study showing that our proposed algorithm is superior to existing versions of PC.

翻译：因果结构学习（CSL），亦称因果发现，旨在从数据中提取变量间的因果关系。CSL使得仅从观测数据中估计因果效应成为可能，从而避免了进行真实实验的需求。基于约束的CSL利用条件独立性检验来执行因果发现。本文提出Shapley-PC，一种通过在所有可能的条件集上应用Shapley值来改进基于约束的CSL算法的新方法，以判定哪些变量应对观测到的条件（非）独立性负责。我们证明了Shapley-PC的可靠性、完备性与渐近一致性，并通过仿真研究表明，所提出的算法优于现有版本的PC算法。