no code implementations • 28 Apr 2024 • Yonghe Zhao, Huiyan Sun
Therefore, this paper proposes a General Causal Inference (GCI) framework specifically designed for cross-sectional observational data, which precisely identifies the key confounding covariates and provides corresponding identification algorithm.
no code implementations • 22 Aug 2023 • Yonghe Zhao, Qiang Huang, Shuai Fu, Huiyan Sun
Most causal inference models based on the POF (CIMs-POF) are designed for eliminating confounding bias and default to an underlying assumption of Confounding Covariates.
no code implementations • 2 Aug 2023 • Yonghe Zhao, Qiang Huang, Siwei Wu, Yun Peng, Huiyan Sun
By disentangling observed and unobserved confounders, VLUCI constructs a doubly variational inference model to approximate the distribution of unobserved confounders, which are used for inferring more accurate counterfactual outcomes.
no code implementations • 24 Jul 2023 • Yonghe Zhao, Qiang Huang, Haolong Zeng, Yun Pen, Huiyan Sun
Extensive experiments on synthetic datasets show that the DRL model performs superiorly in learning de-confounding representations and outperforms state-of-the-art counterfactual inference models for continuous treatment variables.