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 …
Consistency guarantees for greedy permutation-based causal inference algorithms
Directed acyclic graphical models are widely used to represent complex causal systems.
Since the basic task of learning such a model from data is NP-hard, a standard approach is …
Since the basic task of learning such a model from data is NP-hard, a standard approach is …
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
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 …
On the edges of characteristic imset polytopes
S Linusson, P Restadh, L Solus - arxiv preprint arxiv:2209.07579, 2022 - arxiv.org
The edges of the characteristic imset polytope, $\operatorname {CIM} _p $, were recently
shown to have strong connections to causal discovery as many algorithms could be …
shown to have strong connections to causal discovery as many algorithms could be …
Lazyiter: a fast algorithm for counting Markov equivalent DAGs and designing experiments
The causal relationships among a set of random variables are commonly represented by a
Directed Acyclic Graph (DAG), where there is a directed edge from variable $ X $ to variable …
Directed Acyclic Graph (DAG), where there is a directed edge from variable $ X $ to variable …
On the number and size of Markov equivalence classes of random directed acyclic graphs
In causal inference on directed acyclic graphs, the orientation of edges is in general only
recovered up to Markov equivalence classes. We study Markov equivalence classes of …
recovered up to Markov equivalence classes. We study Markov equivalence classes of …
Size of interventional Markov equivalence classes in random DAG models
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 …
Merging joint distributions via causal model classes with low VC dimension
D Janzing - arxiv preprint arxiv:1804.03206, 2018 - arxiv.org
If $ X, Y, Z $ denote sets of random variables, two different data sources may contain
samples from $ P_ {X, Y} $ and $ P_ {Y, Z} $, respectively. We argue that causal inference …
samples from $ P_ {X, Y} $ and $ P_ {Y, Z} $, respectively. We argue that causal inference …