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

Iteration 1 uses a broad, data-driven prior; subsequent iterations exploit memory to execute focused, theory-driven repairs, steadily converging on a causally defensible graph. This iterative loop is made explicit in Algorithm 1, while the statistics used during Evaluate are summarised in Table 2 and computed procedurally in Algorithm 2. 3.1. Causal Assumptions Every proposed DAG must explicitly address the four core assumptions required for causal identification. First, regarding unobserved confounding, the agent must state which latent factors remain and how observed variables serve as proxies for these unobserved influences. Second, the positivity assumption requires that the agent argue no sub-population is locked into or out of the treatment, often demonstrated by reporting overlap in the propensity-score distribution across treatment groups.