Breaking Bad: Component-Wise Parent Deletion for Score-Based Causal Discovery

Directed acyclic graphs (DAGs) are widely used to represent complex causal relationships in real-world systems. The goal of causal discovery is to learn the underlying {DAG} from data generated by these systems. While *Greedy Equivalence Search* (GES) is a well-established score-based algorithm for causal discovery, the GES family often suffers from scalability and sample complexity issues due to its large search space and susceptibility to local optima. In this paper, we introduce *parent deletion*, a novel, simple, yet powerful operator for score-based causal discovery. This operator is theoretically sound and effectively alleviates these limitations. Moreover, its simplicity enables seamless compatibility with existing score-based methods, and extensive experiments demonstrate consistent improvements across a wide range of settings. The source code used in this study is available at https://github.com/LGAI-Research/breaking-bad.