Causal structure learning: A combinatorial perspective

C Squires, C Uhler - Foundations of Computational Mathematics, 2023 - Springer
In this review, we discuss approaches for learning causal structure from data, also called
causal discovery. In particular, we focus on approaches for learning directed acyclic graphs …

Sound and complete causal identification with latent variables given local background knowledge

TZ Wang, T Qin, ZH Zhou - Advances in Neural Information …, 2022 - proceedings.neurips.cc
Great efforts have been devoted to causal discovery from observational data, and it is well
known that introducing some background knowledge attained from experiments or human …

Membership testing in markov equivalence classes via independence queries

J Zhang, K Shiragur, C Uhler - International Conference on …, 2024 - proceedings.mlr.press
Understanding causal relationships between variables is a fundamental problem with broad
impact in numerous scientific fields. While extensive research has been dedicated to\emph …

Active causal structure learning with advice

D Choo, T Gouleakis… - … Conference on Machine …, 2023 - proceedings.mlr.press
We introduce the problem of active causal structure learning with advice. In the typical well-
studied setting, the learning algorithm is given the essential graph for the observational …

Sound and complete causal identification with latent variables given local background knowledge

TZ Wang, T Qin, ZH Zhou - Artificial Intelligence, 2023 - Elsevier
Great efforts have been devoted to causal discovery from observational data, and it is well
known that introducing some background knowledge attained from experiments or human …

Near-optimal multi-perturbation experimental design for causal structure learning

S Sussex, C Uhler, A Krause - Advances in Neural …, 2021 - proceedings.neurips.cc
Causal structure learning is a key problem in many domains. Causal structures can be learnt
by performing experiments on the system of interest. We address the largely unexplored …

Efficient enumeration of markov equivalent dags

M Wienöbst, M Luttermann, M Bannach… - Proceedings of the …, 2023 - ojs.aaai.org
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 …

New metrics and search algorithms for weighted causal DAGs

D Choo, K Shiragur - International Conference on Machine …, 2023 - proceedings.mlr.press
Recovering causal relationships from data is an important problem. Using observational
data, one can typically only recover causal graphs up to a Markov equivalence class and …

A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the Same Skeleton

VS Sharma - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
Causal DAGs (also known as Bayesian networks) are a popular tool for encoding
conditional dependencies between random variables. In a causal DAG, the random …

Do we become wiser with time? On causal equivalence with tiered background knowledge

CW Bang, V Didelez - Uncertainty in Artificial Intelligence, 2023 - proceedings.mlr.press
Equivalence classes of DAGs (represented by CPDAGs) may be too large to provide useful
causal information. Here, we address incorporating tiered background knowledge yielding …