Search Results for author:
Found 6 papers, 0 papers with code
On the Representation of Causal Background Knowledge and its Applications in Causal Inference
Causal background knowledge about the existence or the absence of causal edges and paths is frequently encountered in observational studies.
A Local Method for Identifying Causal Relations under Markov Equivalence
We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs).
On Low Rank Directed Acyclic Graphs and Causal Structure Learning
Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse.
Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphs
A Markov equivalence class can be represented by an essential graph and its undirected subgraphs determine the size of the class.
Supplement to "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs"
This supplementary material includes three parts: some preliminary results, four examples, an experiment, three new algorithms, and all proofs of the results in the paper "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs".
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
In this paper, we design reversible irreducible Markov chains on the space of Markov equivalent classes by proposing a perfect set of operators that determine the transitions of the Markov chain.
