6 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…
Deconfounded Time Series Forecasting: A Causal Inference Approach
Wentao Gao, Xiaojing Du, Wenjun Yu +3
Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making. Traditional forecasting methods often rely on current…
From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction
Wentao Gao, Jiuyong Li, Lin Liu +6
Zero-inflated data pose significant challenges in precipitation forecasting due to the predominance of zeros with sparse non-zero events. To address this, we propose the Zero Infla…
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…
Mitigating Dual Latent Confounding Biases in Recommender Systems
Jianfeng Deng, Qingfeng Chen, Debo Cheng +3
Recommender systems are extensively utilised across various areas to predict user preferences for personalised experiences and enhanced user engagement and satisfaction. Traditiona…