ARCADIA: Scalable Causal Discovery for Corporate Bankruptcy Analysis Using Agentic AI

• URL: http://arxiv.org/abs/2512.00839v1
• Date: Sun, 30 Nov 2025 11:21:29 GMT
• Title: ARCADIA: Scalable Causal Discovery for Corporate Bankruptcy Analysis Using Agentic AI
• Authors: Fabrizio Maturo, Donato Riccio, Andrea Mazzitelli, Giuseppe Bifulco, Francesco Paolone, Iulia Brezeanu,
• Abstract summary: ARCADIA integrates large-language-model reasoning with statistical diagnostics to construct valid causal structures.<n>Unlike traditional algorithms, ARCADIA iteratively refines candidate DAGs through constraint-guided prompting and causal-validity feedback.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces ARCADIA, an agentic AI framework for causal discovery that integrates large-language-model reasoning with statistical diagnostics to construct valid, temporally coherent causal structures. Unlike traditional algorithms, ARCADIA iteratively refines candidate DAGs through constraint-guided prompting and causal-validity feedback, leading to stable and interpretable models for real-world high-stakes domains. Experiments on corporate bankruptcy data show that ARCADIA produces more reliable causal graphs than NOTEARS, GOLEM, and DirectLiNGAM while offering a fully explainable, intervention-ready pipeline. The framework advances AI by demonstrating how agentic LLMs can participate in autonomous scientific modeling and structured causal inference.

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