DriftGuard-AEDL: Concept-Drift-Aware Continual Neuro-Symbolic Causal Inference for Evolving Time Series

Abstract

Structural causal models estimated on non-stationary time series are threatened by concept drift: the data-generating structure itself changes over time, silently invalidating a once-correct model. Recent neuro-symbolic pipelines such as Adaptive Event-Driven Labeling (AEDL) combine language-model reasoning with econometric identification, but they are trained one-shot and assume a fixed causal graph and a static anchor set. We propose DriftGuard-AEDL, a concept-drift-aware, continual extension that (i) estimates a contemporaneous causal graph online under a symbolic prior that encodes a known causal ordering and sign constraints; (ii) detects drift with a two-signal detector that couples a residual Page-Hinkley test with a lagged-reference test on the reduced-form coefficient stream; (iii) uses a symbolic verifier as an acceptance gate that admits an adaptation only when the re-estimated structure is economically coherent and materially changed; and (iv) adapts continually with anchor re-calibration and replay. On a controlled regime-switching structural VAR with known ground truth, DriftGuard-AEDL recovers the causal structure far better than drift-agnostic baselines (structural Hamming distance 1.9 vs. 7.3–8.2 and edge-F1 0.75 vs. ≤0.15) and matches a continuously retrained sliding-window model in predictive error while using 6 updates instead of 114; the symbolic gate cuts the false-alarm rate from 0.71 to 0.50 and the update count from 9.4 to 6.0. On the real Elec2 electricity-market benchmark (45,312 records), it recovers most of the adaptation gain of blind retraining at roughly one-fifth of the update cost and four times the speed. The results show that pairing lightweight symbolic constraints with drift-triggered continual learning yields causal inference that stays valid as the world changes.