Výsledky vyhledávání - "Zhao, Yonghe"

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A General Causal Inference Framework for Cross-Sectional Observational Data

Autor: Zhao, Yonghe, Sun, Huiyan

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, these methods generally assume that covariates consist solely of confound

Externí odkaz: http://arxiv.org/abs/2404.18197

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Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?

Autor: Zhao, Yonghe, Huang, Qiang, Fu, Shuai, Sun, Huiyan

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 confounding bias and default to an underlying assumption of Confoundi

Externí odkaz: http://arxiv.org/abs/2308.11676

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VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference

Autor: Zhao, Yonghe, Huang, Qiang, Wu, Siwei, Peng, Yun, Sun, Huiyan

Causal inference plays a vital role in diverse domains like epidemiology, healthcare, and economics. De-confounding and counterfactual prediction in observational data has emerged as a prominent concern in causal inference research. While existing mo

Externí odkaz: http://arxiv.org/abs/2308.00904

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De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network

Autor: Zhao, Yonghe, Huang, Qiang, Zeng, Haolong, Pen, Yun, Sun, Huiyan

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 methods based on Marginal Structural Model for eliminating the confoun

Externí odkaz: http://arxiv.org/abs/2307.12625

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