Causal structure learning: A combinatorial perspective
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 …
causal discovery. In particular, we focus on approaches for learning directed acyclic graphs …
Designing accountable systems
S Kacianka, A Pretschner - Proceedings of the 2021 ACM conference on …, 2021 - dl.acm.org
Accountability is an often called for property of technical systems. It is a requirement for
algorithmic decision systems, autonomous cyber-physical systems, and for software systems …
algorithmic decision systems, autonomous cyber-physical systems, and for software systems …
Bayesian learning of multiple directed networks from observational data
Graphical modeling represents an established methodology for identifying complex
dependencies in biological networks, as exemplified in the study of co‐expression, gene …
dependencies in biological networks, as exemplified in the study of co‐expression, gene …
Counting and sampling Markov equivalent directed acyclic graphs
T Talvitie, M Koivisto - Proceedings of the AAAI Conference on Artificial …, 2019 - ojs.aaai.org
Exploring directed acyclic graphs (DAGs) in a Markov equivalence class is pivotal to infer
causal effects or to discover the causal DAG via appropriate interventional data. We …
causal effects or to discover the causal DAG via appropriate interventional data. We …
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 …
conditional dependencies between random variables. In a causal DAG, the random …
Markov equivalence of max-linear Bayesian networks
C Améndola, B Hollering… - Uncertainty in Artificial …, 2021 - proceedings.mlr.press
Max-linear Bayesian networks have emerged as highly applicable models for causal
inference from extreme value data. However, conditional independence (CI) for max-linear …
inference from extreme value data. However, conditional independence (CI) for max-linear …
An efficient algorithm for counting Markov equivalent DAGs
We consider the problem of counting the number of DAGs which are Markov equivalent, ie,
which encode the same conditional independencies between random variables. The …
which encode the same conditional independencies between random variables. The …
[HTML][HTML] Counting Markov equivalence classes for DAG models on trees
DAG models are statistical models satisfying a collection of conditional independence
relations encoded by the nonedges of a directed acyclic graph (DAG) G. Such models are …
relations encoded by the nonedges of a directed acyclic graph (DAG) G. Such models are …
Recovering causal structures from low-order conditional independencies
M Wienöbst, M Liskiewicz - Proceedings of the AAAI Conference on …, 2020 - ojs.aaai.org
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 …
conditional independence (CI) relationships between random variables are difficult to …
Size of interventional Markov equivalence classes in random DAG models
D Katz, K Shanmugam, C Squires… - The 22nd International …, 2019 - proceedings.mlr.press
Directed acyclic graph (DAG) models are popular for capturing causal relationships. From
observational and interventional data, a DAG model can only be determined up to its\emph …
observational and interventional data, a DAG model can only be determined up to its\emph …