1 citations · 2 across the 10 of their papers we have counts for
7 papers · 1 filter
Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties
Xiaojing Du, Jiuyong Li, Lin Liu +3
Evaluating network interventions requires understanding how treatment affects people through their social relationships. Counting treated neighbors without distinguishing supportiv…
When Interference Graphs Evolve: Doubly Robust Estimation of Dynamic Peer Effects
Xiaojing Du
Peer effects are difficult to estimate when interaction graphs evolve because pre-assignment network history, dynamic peer exposure, and post-assignment network change have distinc…
Transportable Causal Effect Estimation across Networks under Interference
Xiaojing Du, Jiuyong Li, Lin Liu +3
Estimating causal effects under network interference typically assumes that the network used for training and the network used for deployment coincide. In practice, an intervention…
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…
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…
Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks
Xiaojing Du, Feiyu Yang, Wentao Gao +1
As network data applications continue to expand, causal inference within networks has garnered increasing attention. However, hidden confounders complicate the estimation of causal…