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Found 8 papers, 6 papers with code
Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs
In the context of inferring a Bayesian network structure (directed acyclic graph, DAG for short), we devise a non-reversible continuous time Markov chain, the "Causal Zig-Zag sampler", that targets a probability distribution over classes of observationally equivalent (Markov equivalent) DAGs.
Practical Algorithms for Orientations of Partially Directed Graphical Models
In observational studies, the true causal model is typically unknown and needs to be estimated from available observational and limited experimental data.
Efficient Enumeration of Markov Equivalent DAGs
Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence class (MEC) is an important primitive in causal analysis.
Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs
In 2022, Jeong, Tian, and Bareinboim presented the first polynomial-time algorithm for finding sets satisfying the front-door criterion in a given directed acyclic graph (DAG), with an $O(n^3(n+m))$ run time, where $n$ denotes the number of variables and $m$ the number of edges of the causal graph.
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
Counting and sampling directed acyclic graphs from a Markov equivalence class are fundamental tasks in graphical causal analysis.
Identification in Tree-shaped Linear Structural Causal Models
We investigate models, whose directed component forms a tree, and show that there, besides classical instrumental variables, missing cycles of bidirected edges can be used to identify the model.
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs
Counting and uniform sampling of directed acyclic graphs (DAGs) from a Markov equivalence class are fundamental tasks in graphical causal analysis.
Recovering Causal Structures from Low-Order Conditional Independencies
In this paper, we propose an algorithm which, for a given set of conditional independencies of order less or equal to $k$, where $k$ is a small fixed number, computes a faithful graphical representation of the given set.
