🤖 AI Summary
This work addresses the limitations of existing explainable AI methods, which predominantly focus on associative predictions and fall short in supporting decision-making that requires causal reasoning and counterfactual analysis. To bridge this gap, the paper proposes a novel framework that integrates causal machine learning with intrinsically interpretable models—such as additive models and symbolic regression—by explicitly embedding causal inference mechanisms within the model architecture. This approach enables the explicit recovery of causal structures and functional forms among variables directly from cross-sectional data. While maintaining high predictive accuracy, the method achieves comprehensive transparency in system structure, causal relationships, and response mechanisms, thereby substantially enhancing both interpretability and causal reliability for trustworthy “What-if” analyses.
📝 Abstract
The growing reliance on machine learning for decisions across sectors underscores the importance of model transparency and interpretability. Existing post hoc explainability methods and inherently interpretable approaches shed light on model behavior, yet they primarily reveal how models exploit correlations to maximize performance in prediction tasks. However, many decisions require causal insights and the possibility of using models for what-if scenario evaluation. To address this, we propose the integration of causal machine learning with inherently interpretable models for cross-sectional data. We evaluate these methods in terms of predictive accuracy and interpretability. Our findings show that the proposed approach achieves competitive performance in prediction and what-if analysis while offering transparency on the system structure, causal relationships among variables, and the functional forms that connect them. This work contributes to research on causality, machine learning interpretability, and data-driven decision support by offering informed, transparent, and causally grounded decisions.