The 'causal revolution' in financial decision making: An AI budget optimization framework based on counterfactual reasoning

This paper addresses the core issue of "causal confusion" in traditional financial decision-making by proposing an AI budget optimization framework based on counterfactual reasoning. Grounded in Structural Causal Models (SCM), the framework employs methods like Difference-in-Differences (DID), Instrumental Variables (IV), and counterfactual generative adversarial networks to block confounding paths and solve endogeneity. The framework features a three-layer architecture: the Data Layer identifies confounding variables via Directed Acyclic Graphs (DAGs) and screens causal features using Causal Principal Component Analysis (C-PCA); the Model Layer fuses temporal and causal dynamics with a Dynamic Structural Causal Model (DSCM), generating counterfactual budgets via Monte Carlo simulation to quantify intervention effects and balance interdepartmental competition through multi-agent games; the Decision Layer designs reinforcement learning rewards based on counterfactual ROI, embedding strategic constraints and addressing data drift via online A/B testing. The empirical results of the retail industry show that the causal AI model improves budget allocation efficiency by 18.7% compared to traditional ROI models, successfully corrects confounding bias, and captures nonlinear effects. The conclusion shows that the causal revolution, through the organic combination of counterfactual reasoning and AI, has brought a paradigm shift from "data fitting" to "causal intervention" for financial decision-making, greatly improving the scientific rigor and accuracy of budget optimization.