Distributionally Robust Causal Abstractions

2025-10-06

AI Summary

Existing causal abstraction (CA) methods assume fixed and correctly specified exogenous distributions, rendering them fragile under environmental shifts and model misspecification. Method: We propose the first distributionally robust causal abstraction framework: it models exogenous distributional uncertainty via Wasserstein ambiguity sets, ensuring cross-distribution consistency of multi-granular causal models under interventions; robust learning is achieved through constrained minimax optimization, with the ambiguity set radius principledly governing the robustness level. Contribution/Results: This work pioneers the integration of distributional robustness into causal abstraction, unifying robustness against environmental changes, structural misspecification, and intervention mapping bias. Theoretical analysis provides principled guidance for hyperparameter selection—particularly the ambiguity radius—while empirical evaluation demonstrates significant robustness gains across diverse CA methods and tasks under both distributional shift and model misspecification.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Causal Abstraction (CA) theory provides a principled framework for relating causal models that describe the same system at different levels of granularity while ensuring interventional consistency between them. Recently, several approaches for learning CAs have been proposed, but all assume fixed and well-specified exogenous distributions, making them vulnerable to environmental shifts and misspecification. In this work, we address these limitations by introducing the first class of distributionally robust CAs and their associated learning algorithms. The latter cast robust causal abstraction learning as a constrained min-max optimization problem with Wasserstein ambiguity sets. We provide theoretical results, for both empirical and Gaussian environments, leading to principled selection of the level of robustness via the radius of these sets. Furthermore, we present empirical evidence across different problems and CA learning methods, demonstrating our framework's robustness not only to environmental shifts but also to structural model and intervention mapping misspecification.

Problem

• Addressing vulnerability of causal abstraction learning to environmental shifts
• Developing robust causal abstractions against distributional and structural misspecification
• Solving constrained min-max optimization with Wasserstein ambiguity sets

Innovation

• Distributionally robust causal abstraction learning framework
• Constrained min-max optimization with Wasserstein ambiguity sets
• Robustness to environmental shifts and model misspecification