Causality-Inspired Robustness for Nonlinear Models via Representation Learning

2025-05-19

AI Summary

Existing causal robustness frameworks lack finite-radius theoretical guarantees for nonlinear models. Method: We propose the first causally inspired nonlinear modeling approach with explicit finite-radius distributionally robust guarantees. Our method extends causal robustness to nonlinear settings by integrating identifiable representation learning with causal structure modeling, constructing a data-driven uncertainty set, and embedding it within a distributionally robust optimization framework coupled with nonlinear invariant risk minimization. Contribution/Results: Theoretically, we establish finite-radius robustness guarantees under nonlinear conditions. Empirically, we demonstrate significant improvements in out-of-distribution generalization on both synthetic benchmarks and real-world single-cell transcriptomic datasets. This work provides a novel paradigm for robust causal representation learning in complex biomedical applications.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Ensures distributional robustness in nonlinear prediction models
• Extends causality-inspired robustness to nonlinear settings
• Validates finite-radius robustness on synthetic and real-world data

Innovation

• Uses causality-inspired representation learning for robustness
• Ensures finite-radius robustness in nonlinear settings
• Validated on synthetic and real-world single-cell data