25 citations · 48 across the 5 of their papers we have counts for
5 papers
Continual Causal Inference with Incremental Observational Data
Zhixuan Chu, Ruopeng Li, Stephen Rathbun +1
The era of big data has witnessed an increasing availability of observational data from mobile and social networking, online advertising, web mining, healthcare, education, public…
Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials
Zhixuan Chu, Stephen L. Rathbun, Sheng Li
Estimating treatment effects from observational data provides insights about causality guiding many real-world applications such as different clinical study designs, which are the…
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data
Zhixuan Chu, Stephen Rathbun, Sheng Li
The foremost challenge to causal inference with real-world data is to handle the imbalance in the covariates with respect to different treatment options, caused by treatment select…
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data
Zhixuan Chu, Stephen L. Rathbun, Sheng Li
Treatment effect estimation from observational data is a critical research topic across many domains. The foremost challenge in treatment effect estimation is how to capture hidden…
Matching in Selective and Balanced Representation Space for Treatment Effects Estimation
Zhixuan Chu, Stephen L. Rathbun, Sheng Li
The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference. Howeve…