Neuro-Symbolic Causal Boosting: A Framework for Interpretable Attribution of Business Fluctuations

Attributing business fluctuations to actionable drivers is a critical component of decision-making in high-stakes domains. However, prevailing predictive models, predominantly driven by correlations, often yield inconsistent explanations that degrade under distribution shifts or latent confounding. Empirical causal inference to address this limitation remains challenge due to the identifiability gap in purely data-driven discovery and the complexity of encoding domain knowledge into differentiable learning pipelines. To bridge this gap, we propose Neuro-Symbolic Causal Boosting, a unified framework that integrates semantic domain priors with gradient-based causal estimation. First, we introduce the Complete Cause Identification Algorithm (CCIA). Unlike global search methods, CCIA recursively reconstructs the ancestral causal graph of the target variable by employing Kolmogorov-Arnold Networks (KANs) as high-precision filters for low-order independence testing, coupled with a neuro-symbolic adjudication based on Large Language Model to resolve directionality. Subsequently, the identified structure scaffolds the Causal Additive Boosting Network (CABN). Grounded in the theory of structural identification, CABN enforces a reverse topological learning process. It utilizes weighted KANs to sequentially estimate downstream effects and adjust for confounding, thereby isolating invariant causal mechanisms. Empirical evaluation on a real-world telesales dataset and synthetic benchmarks demonstrates that our framework achieves a 57.5% reduction in Out-of-Distribution prediction error compared to strong correlation-based baselines. Additionally, it identifies and quantifies the impact of actionable drivers, providing a structured approach from observational data to trustworthy and interpretable business strategies.