Causal Structure Learning (CSL), amounting to extracting causal relations among the variables in a dataset, is widely perceived as an important step towards robust and transparent models. 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 and asymptotic consistency and demonstrate that it can outperform state-of-the-art constraint-based, search-based and functional causal model-based methods, according to standard metrics in CSL.

翻译：因果结构学习（Causal Structure Learning, CSL）旨在提取数据集中变量间的因果关系，被广泛视为构建鲁棒且可解释模型的关键步骤。基于约束的CSL方法利用条件独立性检验进行因果发现。我们提出Shapley-PC，一种通过使用Shapley值对可能的条件集进行重要性评估，从而判定哪些变量导致了观测到的条件（不）依赖关系，进而改进基于约束的CSL算法的新方法。我们证明了该方法的理论正确性与渐近一致性，并验证了其在CSL标准指标上优于当前最先进的基于约束、基于搜索及基于功能因果模型的方法。