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

Fabrizio Maturo; Donato Riccio; Andrea Mazzitelli; Giuseppe Bifulco; Francesco Paolone; Iulia Brezeanu

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arXiv · 2512.00839

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

Fabrizio Maturo·Donato Riccio·Andrea Mazzitelli·Giuseppe Bifulco·Francesco Paolone·Iulia Brezeanu

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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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Fabrizio Maturo, Donato Riccio, Andrea Mazzitelli, Giuseppe Bifulco, Francesco Paolone, Iulia Brezeanu. 2025-11-30. ARCADIA: Scalable Causal Discovery for Corporate Bankruptcy Analysis Using Agentic AI. https://arxiv.org/abs/2512.00839

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