6 papers · 1 filter
Learning Fair Graph Representations with Multi-view Information Bottleneck
Chuxun Liu, Debo Cheng, Qingfeng Chen +3
Graph neural networks (GNNs) excel on relational data by passing messages over node features and structure, but they can amplify training data biases, propagating discriminatory at…
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…
Disentangled Representation Learning for Causal Inference with Instruments
Debo Cheng, Jiuyong Li, Lin Liu +4
Latent confounders are a fundamental challenge for inferring causal effects from observational data. The instrumental variable (IV) approach is a practical way to address this chal…
Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data
Debo Cheng, Ziqi Xu, Jiuyong Li +4
Querying causal effects from time-series data is important across various fields, including healthcare, economics, climate science, and epidemiology. However, this task becomes com…
TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
Wentao Gao, Ziqi Xu, Jiuyong Li +6
As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting be…
Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
Yang Xie, Ziqi Xu, Debo Cheng +4
Estimating causal effects from observational data is challenging, especially in the presence of latent confounders. Much work has been done on addressing this challenge, but most o…