Causally Inspired Regularization Enables Domain General Representations
<|reference_start|>Causally Inspired Regularization Enables Domain General Representations: Given a causal graph representing the data-generating process shared across different domains/distributions, enforcing sufficient graph-implied conditional independencies can identify domain-general (non-spurious) feature representations. For the standard input-output predictive setting, we categorize the set of graphs considered in the literature into two distinct groups: (i) those in which the empirical risk minimizer across training domains gives domain-general representations and (ii) those where it does not. For the latter case (ii), we propose a novel framework with regularizations, which we demonstrate are sufficient for identifying domain-general feature representations without a priori knowledge (or proxies) of the spurious features. Empirically, our proposed method is effective for both (semi) synthetic and real-world data, outperforming other state-of-the-art methods in average and worst-domain transfer accuracy.<|reference_end|>
@article{salaudeen2024causally,
    title={Causally Inspired Regularization Enables Domain General Representations},
    author={Olawale Salaudeen and Sanmi Koyejo},
    journal={arXiv preprint arXiv:2404.16277},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16277},
    primaryClass={cs.LG stat.ML}
}