1 code implementation • 30 Apr 2024 • Haoxuan Li, Chunyuan Zheng, Sihao Ding, Peng Wu, Zhi Geng, Fuli Feng, Xiangnan He
On this basis, we propose a novel ideal loss that can be used to deal with selection bias in the presence of neighborhood effect.
1 code implementation • 30 Apr 2024 • Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Peng Wu, Zhi Geng, Xu Chen, Peng Cui
Inspired by these gaps, we propose to approximate the balancing functions in reproducing kernel Hilbert space and demonstrate that, based on the universal property and representer theorem of kernel functions, the causal balancing constraints can be better satisfied.
no code implementations • 13 Aug 2023 • Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang
To address this, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured variables and some other measured variables.
1 code implementation • 17 Feb 2022 • Yinan Lin, Wen Zhou, Zhi Geng, Gexin Xiao, Jianxin Yin
Both the threshold points and regression coefficients are unknown and to be estimated.
no code implementations • 25 Feb 2021 • Zhuangyan Fang, Yue Liu, Zhi Geng, Shengyu Zhu, Yangbo He
We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs).
no code implementations • 27 Oct 2016 • Yango He, Zhi Geng
Then for the case that the sample sizes of these interventions are relatively small, we propose a data-pooling method for learning causal networks in which we pool all data sets of these interventions together for the learning.