no code implementations • 3 Mar 2023 • Zhixuan Chu, Ruopeng Li, Stephen Rathbun, Sheng Li
We propose a Continual Causal Effect Representation Learning method for estimating causal effects with observational data, which are incrementally available from non-stationary data distributions.
1 code implementation • 10 Feb 2023 • Lei Zhang, Xiaodong Yan, Jianshan He, Ruopeng Li, Wei Chu
Our experimental results show that our model effectively relieves the problem of over-smoothing in deep GCNs and outperforms the state-of-the-art (SOTA) methods on various benchmark datasets.
no code implementations • 2 Feb 2023 • Zhixuan Chu, Jianmin Huang, Ruopeng Li, Wei Chu, Sheng Li
Causal inference has numerous real-world applications in many domains, such as health care, marketing, political science, and online advertising.
no code implementations • 2 Jan 2023 • Yacheng He, Qianghuai Jia, Lin Yuan, Ruopeng Li, Yixin Ou, Ningyu Zhang
This paper illustrates the technologies of user next intent prediction with a concept knowledge graph.
1 code implementation • 3 Apr 2022 • Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li, Wei Chu
In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion.