32 citations · 60 across the 9 of their papers we have counts for
4 papers · 1 filter
A Local Method for Identifying Causal Relations under Markov Equivalence
Zhuangyan Fang, Yue Liu, Zhi Geng +2
Causality is important for designing interpretable and robust methods in artificial intelligence research. We propose a local approach to identify whether a variable is a cause of…
Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphs
Yangbo He, Bin Yu
The sizes of Markov equivalence classes of directed acyclic graphs play important roles in measuring the uncertainty and complexity in causal learning. A Markov equivalence class c…
Supplement to "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs"
Yangbo He, Jinzhu Jia, Bin Yu
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 "Reversib…
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Yangbo He, Jinzhu Jia, Bin Yu
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are sai…