3 citations · 4 across the 6 of their papers we have counts for
7 papers
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…
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…
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…
Practical Algorithms for Orientations of Partially Directed Graphical Models
Malte Luttermann, Marcel Wienöbst, Maciej Liśkiewicz
In observational studies, the true causal model is typically unknown and needs to be estimated from available observational and limited experimental data. In such cases, the learne…
Identification in Tree-shaped Linear Structural Causal Models
Benito van der Zander, Marcel Wienöbst, Markus Bläser +1
Linear structural equation models represent direct causal effects as directed edges and confounding factors as bidirected edges. An open problem is to identify the causal parameter…
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs
Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz
Counting and uniform sampling of directed acyclic graphs (DAGs) from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper, we show that thes…
Recovering Causal Structures from Low-Order Conditional Independencies
Marcel Wienöbst, Maciej Liśkiewicz
One of the common obstacles for learning causal models from data is that high-order conditional independence (CI) relationships between random variables are difficult to estimate.…