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stat.ME2025
Causal Inference for Network Autoregression Model: A Targeted Minimum Loss Estimation Approach
Yong Wu, Shuyuan Wu, Xinwei Sun +1
We study estimation of the average treatment effect (ATE) from a single network in observational settings with interference. The weak cross-unit dependence is modeled via an endoge…
stat.ME2025
Discovering Causal Relationships using Proxy Variables under Unmeasured Confounding
Yong Wu, Yanwei Fu, Shouyan Wang +2
Inferring causal relationships between variable pairs in the observational study is crucial but challenging, due to the presence of unmeasured confounding. While previous methods e…
stat.ME2023
Doubly Robust Proximal Causal Learning for Continuous Treatments
Yong Wu, Yanwei Fu, Shouyan Wang +1
Proximal causal learning is a promising framework for identifying the causal effect under the existence of unmeasured confounders. Within this framework, the doubly robust (DR) est…