Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning
Cai, Ruichu Wan, Junjie Chen, Weilin Yang, Zeqin Li, Zijian Zhen, Peng Guo, Jiecheng
Cai, Ruichu
Wan, Junjie
Chen, Weilin
Yang, Zeqin
Li, Zijian
Zhen, Peng
Guo, Jiecheng
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Estimating long-term causal effects by combining long-term observational and short-term experimental data is a crucial but challenging problem in many real-world scenarios. In existing methods, several ideal assumptions, e.g. latent unconfoundedness assumption or additive equi-confounding bias assumption, are proposed to address the latent confounder problem raised by the observational data. However, in real-world applications, these assumptions are typically violated which limits their practical effectiveness. In this paper, we tackle the problem of estimating the long-term individual causal effects without the aforementioned assumptions. Specifically, we propose to utilize the natural heterogeneity of data, such as data from multiple sources, to identify latent confounders, thereby significantly avoiding reliance on idealized assumptions. Practically, we devise a latent representation learning-based estimator of long-term causal effects. Theoretically, we establish the identifiability of latent confounders, with which we further achieve long-term effect identification. Extensive experimental studies, conducted on multiple synthetic and semi-synthetic datasets, demonstrate the effectiveness of our proposed method.
Citation
R. Cai et al., “Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning,” Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, vol. 1, pp. 4788–4796, Sep. 2025, doi: 10.24963/IJCAI.2025/533
Source
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Conference
34 International Joint Conference on Artificial Intelligence (IJCAI-25)
Keywords
Causality
Subjects
Source
34 International Joint Conference on Artificial Intelligence (IJCAI-25)
Publisher
International Joint Conferences on Artificial Intelligence