Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning

Marcel Wienöbst, Leonard Henckel, Sebastian Weichwald

Keywords: Causal Discovery, Bayesian Networks, DAGs, Structure Learning

TL;DR: Discrete graph search is a reasonable structure learning approach enabling accurate causal discovery; the size of the graph space was not the barrier.

Abstract: We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score updates, cutting run-times over prior algorithms. This makes it feasible to fully embrace discrete search, enabling iterated local search with principled order initialization to find graphs with scores at or close to the global optimum. The resulting structures are highly accurate across benchmarks, with near-perfect recovery in standard settings. This performance calls for revisiting discrete search over graphs as a reasonable approach to causal discovery.

Primary Area: causal reasoning

Submission Number: 7339