8 papers
Identifying the Group to Intervene on to Maximise Effect Under Cross-Group Interference
Xiaojing Du, Jiuyong Li, Lin Liu +3
In many networked systems, interventions applied to one group of units can induce substantial causal effects on another group through cross-group interference pathways. Despite its…
Peer Effect Estimation in the Presence of Simultaneous Feedback and Unobserved Confounders
Xiaojing Du, Jiuyong Li, Lin Liu +2
Estimating peer causal effects within complex real-world networks such as social networks is challenging, primarily due to simultaneous feedback between peers and unobserved confou…
Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction
Wentao Gao, Jiuyong Li, Debo Cheng +7
Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, the GCM Outputs exhibit systematic biases due to model unce…
A Novel Generative Model with Causality Constraint for Mitigating Biases in Recommender Systems
Jianfeng Deng, Qingfeng Chen, Debo Cheng +3
Accurately predicting counterfactual user feedback is essential for building effective recommender systems. However, latent confounding bias can obscure the true causal relationshi…
Mitigating Propensity Bias of Large Language Models for Recommender Systems
Guixian Zhang, Guan Yuan, Debo Cheng +3
The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e.g., descriptions and analy…
Counterfactual Samples Constructing and Training for Commonsense Statements Estimation
Chong Liu, Zaiwen Feng, Lin Liu +5
Plausibility Estimation (PE) plays a crucial role for enabling language models to objectively comprehend the real world. While large language models (LLMs) demonstrate remarkable c…