5 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…