Generalized Invariant Matching Property via LASSO
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Abstract (arXiv)
Learning under distribution shifts is a challenging task. One principled approach is to exploit the invariance principle via the structural causal models. However, the invariance principle is violated when the response is intervened, making it a difficult setting. In a recent work, the invariant matching property has been developed to shed light on this scenario and shows promising performance. In this work, by formulating a high-dimensional problem with intrinsic sparsity, we generalize the invariant matching property for an important setting when only the target is intervened. We propose a more robust and computation-efficient algorithm by leveraging a variant of Lasso, improving upon the existing algorithms.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 14 Jan 2023.
Builds on
13 of 24 references.
References · 13
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- 2022Learning Invariant Representations under General Interventions on the Responsecited by 4
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Citing papers per year: 2024: 2.
2 papers.