6 citations · 7 across the 4 of their papers we have counts for
4 papers
A General Causal Inference Framework for Cross-Sectional Observational Data
Yonghe Zhao, Huiyan Sun
Causal inference methods for observational data are highly regarded due to their wide applicability. While there are already numerous methods available for de-confounding bias, the…
VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference
Yonghe Zhao, Qiang Huang, Siwei Wu +2
Causal inference plays a vital role in diverse domains like epidemiology, healthcare, and economics. De-confounding and counterfactual prediction in observational data has emerged…
Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?
Yonghe Zhao, Qiang Huang, Shuai Fu +1
The Potential Outcome Framework (POF) plays a prominent role in the field of causal inference. Most causal inference models based on the POF (CIMs-POF) are designed for eliminating…
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
Yonghe Zhao, Qiang Huang, Haolong Zeng +2
Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. While there are already some sample reweighting…