43 citations · 90 across the 12 of their papers we have counts for
14 papers
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…
Efficient Enumeration of Markov Equivalent DAGs
Marcel Wienöbst, Malte Luttermann, Max Bannach +1
Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence class (MEC) is an important primitive in causal analysis. The central resource from the perspective of comput…
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz
Counting and sampling directed acyclic graphs from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper we show that these tasks can be perf…
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.…