  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.
