55 citations · 70 across the 4 of their papers we have counts for
5 papers
On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference
Zhuangyan Fang, Ruiqi Zhao, Yue Liu +1
Pairwise causal background knowledge about the existence or absence of causal edges and paths is frequently encountered in observational studies. Such constraints allow the shared…
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…
On Low Rank Directed Acyclic Graphs and Causal Structure Learning
Zhuangyan Fang, Shengyu Zhu, Jiji Zhang +3
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…
A Graph Autoencoder Approach to Causal Structure Learning
Ignavier Ng, Shengyu Zhu, Zhitang Chen +1
Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theor…
Masked Gradient-Based Causal Structure Learning
Ignavier Ng, Shengyu Zhu, Zhuangyan Fang +3
This paper studies the problem of learning causal structures from observational data. We reformulate the Structural Equation Model (SEM) with additive noises in a form parameterize…