Publications (9)
CauSTream: Causal Spatio-Temporal Representation Learning for Streamflow Forecasting
Shu Wan, Reepal Shah, John Sabo +2
Streamflow forecasting is crucial for water resource management and risk mitigation. While deep learning models have achieved strong predictive performance, they often overlook und…
Causality Guided Representation Learning for Cross-Style Hate Speech Detection
Chengshuai Zhao, Shu Wan, Paras Sheth +3
The proliferation of online hate speech poses a significant threat to the harmony of the web. While explicit hate is easily recognized through overt slurs, implicit hate speech is…
Long-term Causal Effects Estimation via Latent Surrogates Representation Learning
Ruichu Cai, Weilin Chen, Zeqin Yang +4
Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e.g., marketing and medicine. Despite i…
Proxy-Guided Measurement Calibration
Saketh Vishnubhatla, Shu Wan, Andre Harrison +2
Aggregate outcome variables collected through surveys and administrative records are often subject to systematic measurement error. For instance, in disaster loss databases, county…
DAGverse: Building Document-Grounded Semantic DAGs from Scientific Papers
Shu Wan, Saketh Vishnubhatla, Iskander Kushbay +4
Directed Acyclic Graphs (DAGs) are widely used to represent structured knowledge in scientific and technical domains. However, datasets for real-world DAGs remain scarce because co…
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace
Shu Wan, Chen Zheng, Zhonggen Sun +4
Uplift modeling is a rapidly growing approach that utilizes causal inference and machine learning methods to directly estimate the heterogeneous treatment effects, which has been w…