3 papers
stat.ML2026
Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
Marcel Wienöbst, Leonard Henckel, Sebastian Weichwald
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…
cs.AI2025
Linear-Time Primitives for Algorithm Development in Graphical Causal Inference
Marcel Wienöbst, Sebastian Weichwald, Leonard Henckel
We introduce CIfly, a framework for efficient algorithmic primitives in graphical causal inference that isolates reachability as a reusable core operation. It builds on the insight…
stat.ML2024
Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs
Moritz Schauer, Marcel Wienöbst
In the context of inferring a Bayesian network structure (directed acyclic graph, DAG for short), we devise a non-reversible continuous time Markov chain, the ``Causal Zig-Zag samp…