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arXiv · 2404.16277

Causally Inspired Regularization Enables Domain General Representations

Olawale Salaudeen·Sanmi Koyejo
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Abstract

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.

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Olawale Salaudeen, Sanmi Koyejo. 2024-04-25. Causally Inspired Regularization Enables Domain General Representations. https://arxiv.org/abs/2404.16277

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