77 citations · 124 across the 8 of their papers we have counts for
6 papers · 1 filter
Global Graph Counterfactual Explanation: A Subgraph Mapping Approach
Yinhan He, Wendy Zheng, Yaochen Zhu +5
Graph Neural Networks (GNNs) have been widely deployed in various real-world applications. However, most GNNs are black-box models that lack explanations. One strategy to explain G…
A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection
Jing Ma, Chen Chen, Anil Vullikanti +4
Methicillin-resistant Staphylococcus aureus (MRSA) is a type of bacteria resistant to certain antibiotics, making it difficult to prevent MRSA infections. Among decades of efforts…
Learning for Counterfactual Fairness from Observational Data
Jing Ma, Ruocheng Guo, Aidong Zhang +1
Fairness-aware machine learning has attracted a surge of attention in many domains, such as online advertising, personalized recommendation, and social media analysis in web applic…
CLEAR: Generative Counterfactual Explanations on Graphs
Jing Ma, Ruocheng Guo, Saumitra Mishra +2
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted lab…
Learning Causal Effects on Hypergraphs
Jing Ma, Mengting Wan, Longqi Yang +3
Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existi…
Learning Fair Node Representations with Graph Counterfactual Fairness
Jing Ma, Ruocheng Guo, Mengting Wan +3
Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender. Among the many existin…