ARCADIA: Scalable Causal Discovery for Corporate Bankruptcy Analysis Using Agentic AI
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909934839398400 |
|---|---|
| author | Maturo, Fabrizio Riccio, Donato Mazzitelli, Andrea Bifulco, Giuseppe Paolone, Francesco Brezeanu, Iulia |
| author_facet | Maturo, Fabrizio Riccio, Donato Mazzitelli, Andrea Bifulco, Giuseppe Paolone, Francesco Brezeanu, Iulia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00839 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARCADIA: Scalable Causal Discovery for Corporate Bankruptcy Analysis Using Agentic AI Maturo, Fabrizio Riccio, Donato Mazzitelli, Andrea Bifulco, Giuseppe Paolone, Francesco Brezeanu, Iulia Artificial Intelligence Computation Methodology 62A99, 68T99, 68T05 I.2.6; I.2.0; I.2.4; G.3 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. |
| title | ARCADIA: Scalable Causal Discovery for Corporate Bankruptcy Analysis Using Agentic AI |
| topic | Artificial Intelligence Computation Methodology 62A99, 68T99, 68T05 I.2.6; I.2.0; I.2.4; G.3 |
| url | https://arxiv.org/abs/2512.00839 |