61 citations · 76 across the 5 of their papers we have counts for
5 papers
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…
IMBENS: Ensemble Class-imbalanced Learning in Python
Zhining Liu, Jian Kang, Hanghang Tong +1
imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem. It provides stand…
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
Qiang Huang, Makoto Yamada, Yuan Tian +3
Graph structured data has wide applicability in various domains such as physics, chemistry, biology, computer vision, and social networks, to name a few. Recently, graph neural net…