31 citations · 78 across the 15 of their papers we have counts for
15 papers
Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
Xiangmeng Wang, Qian Li, Haiyang Xia +3
Heterophily is a prevalent property of real-world graphs and is well known to impair the performance of homophilic Graph Neural Networks (GNNs). Prior work has attempted to adapt G…
Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring
Sirui Huang, Jing Long, Qian Li +2
Sequential Recommendation (SR) predicts users next interactions by modeling the temporal order of their historical behaviors. Existing approaches, including traditional sequential…
Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
Zhaoyang Chu, Yao Wan, Qian Li +5
Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding…
Counterfactual Explanation for Fairness in Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +2
Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems.Explaining the causes of unfair recommendations is critical, as it promot…
Neural Causal Graph Collaborative Filtering
Xiangmeng Wang, Qian Li, Dianer Yu +2
Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF). One…
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows
Tri Dung Duong, Qian Li, Guandong Xu
Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as ins…
Achieving Counterfactual Fairness with Imperfect Structural Causal Model
Tri Dung Duong, Qian Li, Guandong Xu
Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i…
Reinforced Path Reasoning for Counterfactual Explainable Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +1
Counterfactual explanations interpret the recommendation mechanism via exploring how minimal alterations on items or users affect the recommendation decisions. Existing counterfact…
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning
Haoran Yang, Hongxu Chen, Sixiao Zhang +4
Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation learning. The key to the success of graph contrastive learning is to acquire high-qu…
Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +3
Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' t…
Deep Treatment-Adaptive Network for Causal Inference
Qian Li, Zhichao Wang, Shaowu Liu +2
Causal inference is capable of estimating the treatment effect (i.e., the causal effect of treatment on the outcome) to benefit the decision making in various domains. One fundamen…
Stochastic Intervention for Causal Inference via Reinforcement Learning
Tri Dung Duong, Qian Li, Guandong Xu
Causal inference methods are widely applied in various decision-making domains such as precision medicine, optimal policy and economics. Central to causal inference is the treatmen…
Stochastic Intervention for Causal Effect Estimation
Tri Dung Duong, Qian Li, Guandong Xu
Causal inference methods are widely applied in various decision-making domains such as precision medicine, optimal policy and economics. Central to these applications is the treatm…
Be Causal: De-biasing Social Network Confounding in Recommendation
Qian Li, Xiangmeng Wang, Guandong Xu
In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A com…
Causality-based Counterfactual Explanation for Classification Models
Tri Dung Duong, Qian Li, Guandong Xu
Counterfactual explanation is one branch of interpretable machine learning that produces a perturbation sample to change the model's original decision. The generated samples can ac…