Hi!

I'm a computer scientist working on problems in causal inference, focusing mainly on the algorithmic aspects of graphical causal models. I have a passion for problem-solving and look for simple and effective solutions.

Picture of me :)
Recent Preprints

Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs
M. Schauer, M. Wienöbst

Selected Publications

Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs
M. Wienöbst, B. van der Zander, M. Liśkiewicz (AAAI 2024)

Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
M. Wienöbst, M. Bannach, M. Liśkiewicz (JMLR 24(213), 2023)

Efficient Enumeration of Markov Equivalent DAGs
M. Wienöbst, M. Luttermann, M. Bannach, M. Liśkiewicz (AAAI 2023: Oral)

A New Constructive Criterion for Markov Equivalence of MAGs
M. Wienöbst, M. Bannach, M. Liśkiewicz (UAI 2022: Oral + Best Student Paper)

Extendability of Causal Graphical Models: Algorithms and Computational Complexity
M. Wienöbst, M. Bannach, M. Liśkiewicz (UAI 2021: Long Talk + Best Student Paper)

Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs
M. Wienöbst, M. Bannach, M. Liśkiewicz (AAAI 2021: Distinguished Paper)

Recovering Causal Structures from Low-Order Conditional Independencies
M. Wienöbst, M. Liśkiewicz (AAAI 2020)