most citedA General Causal Inference Framework for Cross-Sectional Observational Data

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stat.ME2024★ 1 cited

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…

stat.ME2023

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…