Hi! I am a researcher in Causality. Currently, I am working towards a PhD in Computer Science at the University of Lübeck. My main interests lie in developing practical and efficient algorithms for causal inference and analysis. A major share of my research has been about Markov equivalence classes of DAGs, which typically arise in causal discovery from observational data. In short, a Markov equivalence class consists of causal structures, which cannot be distinguished based on observations alone. A focus has been on the algorithmic aspects of Markov equivalence. For example, we discovered how to compute the size of a Markov equivalence class in polynomial time, a fundamental problem with important applications. I am also interested in (algorithmic) problems in other areas of causal inferences and recently started to work on causal models with latent variables, such as maximal ancestral graphs (MAGs), and delved into the problem of causal identification using front-door adjustment.

## Recent Preprints

Finding Front-Door Adjustment Sets in Linear Time

M. Wienöbst, B. van der Zander, M. Liśkiewicz

Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications

M. Wienöbst, M. Bannach, M. Liśkiewicz

## List of Selected Publications

Efficient Enumeration of Markov Equivalent DAGs

M. Wienöbst, M. Luttermann, M. Bannach, M. Liśkiewicz (To appear in AAAI 2023, Oral presentation)

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)