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

doi:10.24963/ijcai.2025/533

Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning

Ruichu Cai, Junjie Wan, Weilin Chen, Zeqin Yang, Zijian Li, Peng Zhen, Jiecheng Guo

Download BibTeX Cite

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4788-4796.

https://doi.org/10.24963/ijcai.2025/533

Keywords

Knowledge Representation and Reasoning: KRR: Causality
Machine Learning: ML: Causality

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.

Ruichu Cai, Junjie Wan, Weilin Chen, Zeqin Yang, Zijian Li, Peng Zhen, and Jiecheng Guo. 2025. Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 4788–4796. https://doi.org/10.24963/ijcai.2025/533

@inproceedings{ijcai2025p533,
  title     = {Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning},
  author    = {Cai, Ruichu and Wan, Junjie and Chen, Weilin and Yang, Zeqin and Li, Zijian and Zhen, Peng and Guo, Jiecheng},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {4788--4796},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/533},
  url       = {https://doi.org/10.24963/ijcai.2025/533},
}

TY  - CPAPER
TI  - Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning
AU  - Cai, Ruichu
AU  - Wan, Junjie
AU  - Chen, Weilin
AU  - Yang, Zeqin
AU  - Li, Zijian
AU  - Zhen, Peng
AU  - Guo, Jiecheng
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 4788
EP  - 4796
DO  - 10.24963/ijcai.2025/533
UR  - https://doi.org/10.24963/ijcai.2025/533
ER  -