Identifiable Latent Neural Causal Models
<|reference_start|>Identifiable Latent Neural Causal Models: Causal representation learning seeks to uncover latent, high-level causal representations from low-level observed data. It is particularly good at predictions under unseen distribution shifts, because these shifts can generally be interpreted as consequences of interventions. Hence leveraging {seen} distribution shifts becomes a natural strategy to help identifying causal representations, which in turn benefits predictions where distributions are previously {unseen}. Determining the types (or conditions) of such distribution shifts that do contribute to the identifiability of causal representations is critical. This work establishes a {sufficient} and {necessary} condition characterizing the types of distribution shifts for identifiability in the context of latent additive noise models. Furthermore, we present partial identifiability results when only a portion of distribution shifts meets the condition. In addition, we extend our findings to latent post-nonlinear causal models. We translate our findings into a practical algorithm, allowing for the acquisition of reliable latent causal representations. Our algorithm, guided by our underlying theory, has demonstrated outstanding performance across a diverse range of synthetic and real-world datasets. The empirical observations align closely with the theoretical findings, affirming the robustness and effectiveness of our approach.<|reference_end|>
@article{liu2024identifiable,
    title={Identifiable Latent Neural Causal Models},
    author={Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton
  van den Hengel, Kun Zhang, Javen Qinfeng Shi},
    journal={arXiv preprint arXiv:2403.15711},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15711},
    primaryClass={cs.LG stat.ME stat.ML}
}