A Causal Theoretical Framework for Open Set Domain Adaptation

ICLR 2025 Conference Submission9422 Authors

Keywords: Causal theory, open set domain adaptation, domain adaptation, empirical risk minimization

Abstract:

Open Set Domain Adaptation (OSDA) faces two critical challenges: the emergence
of unknown classes in the target domain and changes in observed distributions
across domains. Although numerous studies have proposed advanced algorithms,
recent experimental results demonstrate that the classical Empirical Risk Minimization (ERM) approach still delivers state-of-the-art performance. However,
few theories can effectively explain this disputed phenomenon. To address the
theoretical gap, we focus on constructing a causal theoretical framework for OSDA.
We formulate the novel concepts of the Fully Informative Causal Invariance Model
(FICIM) and the Partially Informative Causal Invariance Model (PICIM). Subsequently, We derive an OSDA theoretical bound to prove that the ERM performs
well when the source domain follows FICIM, while it performs poorly when the
source domain follows PICIM. The different results may be attributed to the varying amounts of available information when bounding the target domain’s stable
expected risk. Finally, across different datasets, we conduct extensive experiments
on the FICIM and PICIM source domains to validate the effectiveness of our
theoretical results.

Primary Area: causal reasoning

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Submission Number: 9422