Conformal-AEDL: Uncertainty-Calibrated Neuro-Symbolic Multiagent Causal Inference for Non-Stationary Time Series

Abstract

Event-driven causal labeling in non-stationary time series is useful only when the system can state when its own root-cause decision is ambiguous. Existing neuro-symbolic multiagent pipelines improve semantic and structural consistency, but their confidence scores need not retain a stable error meaning after regime changes. This paper presents Conformal-AEDL, a resource-efficient extension that couples heterogeneous residual and propagation agents with reliability-weighted consensus, an explicit symbolic compliance layer, and online conformal prediction sets. The method outputs a set of plausible root causes rather than forcing a singleton, and adapts the conformal error budget when score surprises indicate rapid drift. Evaluation uses a nonlinear six-node structural causal model with four regimes and a semi-synthetic U.S. macroeconomic benchmark whose dynamics and residuals are estimated from 203 public quarterly observations while intervention labels remain controlled. Across ten seeds, Conformal-AEDL attains 0.929±0.022 and 0.917±0.029 coverage at a nominal 0.90 level, with average set sizes of 1.186 and 1.890. Relative to static split conformal prediction, this reduces set size by 26.0% and 29.5%, respectively. The results show that calibrated abstention can be added without inventing causal ground truth or relying on proprietary language-model calls, while also exposing an important limitation: immediate post-switch coverage remains difficult in the macro benchmark.