Causality-Inspired Robustness for Nonlinear Models via Representation Learning

• URL: http://arxiv.org/abs/2505.12868v1
• Date: Mon, 19 May 2025 08:52:15 GMT
• Title: Causality-Inspired Robustness for Nonlinear Models via Representation Learning
• Authors: Marin Šola, Peter Bühlmann, Xinwei Shen,
• Abstract summary: Distributional robustness is a central goal of prediction algorithms due to the prevalent distribution shifts in real-world data.<n>We propose a nonlinear method under a causal framework by incorporating recent developments in identifiable representation learning.<n>To our best knowledge, this is the first causality-inspired robustness method with such a finite-radius robustness guarantee in nonlinear settings.
• Score: 4.64479351797195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributional robustness is a central goal of prediction algorithms due to the prevalent distribution shifts in real-world data. The prediction model aims to minimize the worst-case risk among a class of distributions, a.k.a., an uncertainty set. Causality provides a modeling framework with a rigorous robustness guarantee in the above sense, where the uncertainty set is data-driven rather than pre-specified as in traditional distributional robustness optimization. However, current causality-inspired robustness methods possess finite-radius robustness guarantees only in the linear settings, where the causal relationships among the covariates and the response are linear. In this work, we propose a nonlinear method under a causal framework by incorporating recent developments in identifiable representation learning and establish a distributional robustness guarantee. To our best knowledge, this is the first causality-inspired robustness method with such a finite-radius robustness guarantee in nonlinear settings. Empirical validation of the theoretical findings is conducted on both synthetic data and real-world single-cell data, also illustrating that finite-radius robustness is crucial.

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