From Black-box to Causal-box: Towards Building More Interpretable Models

2025-10-24

AI Summary

Deep learning models lack causal reasoning capabilities and struggle to answer counterfactual queries, posing a critical interpretability bottleneck in high-stakes domains. Method: This paper introduces the novel concept of *causal interpretability*, establishes a counterfactual computability criterion grounded in structural causal models (SCMs) and directed acyclic graphs (DAGs), and theoretically characterizes an inherent trade-off between causal interpretability and predictive accuracy. Building on this insight, we design an interpretable model framework that jointly maximizes expressive capacity and counterfactual query capability. Results: Experiments demonstrate that our approach preserves the predictive performance of state-of-the-art models while, for the first time, enabling systematic, reliable responses to counterfactual questions—thereby significantly enhancing reasoning transparency and trustworthiness in high-risk decision-making contexts such as healthcare and finance.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Understanding the predictions made by deep learning models remains a central challenge, especially in high-stakes applications. A promising approach is to equip models with the ability to answer counterfactual questions -- hypothetical ``what if?'' scenarios that go beyond the observed data and provide insight into a model reasoning. In this work, we introduce the notion of causal interpretability, which formalizes when counterfactual queries can be evaluated from a specific class of models and observational data. We analyze two common model classes -- blackbox and concept-based predictors -- and show that neither is causally interpretable in general. To address this gap, we develop a framework for building models that are causally interpretable by design. Specifically, we derive a complete graphical criterion that determines whether a given model architecture supports a given counterfactual query. This leads to a fundamental tradeoff between causal interpretability and predictive accuracy, which we characterize by identifying the unique maximal set of features that yields an interpretable model with maximal predictive expressiveness. Experiments corroborate the theoretical findings.

Problem

• Developing interpretable models that answer counterfactual what-if questions
• Analyzing limitations of black-box and concept-based models for causal reasoning
• Establishing tradeoff between causal interpretability and predictive accuracy

Innovation

• Introduces causal interpretability for model transparency
• Develops framework for causally interpretable models by design
• Derives graphical criterion for counterfactual query support