id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-7442	Zhou, Xiaohua 	The 'causal revolution' in financial decision making: An AI budget optimization framework based on counterfactual reasoning	2025	19	.pdf	application/pdf	9348	411	39	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. Intervention distribution modeling: defining counterfactual budget allocation𝑋𝑐𝑓The feasible range, for example:𝑋𝑐𝑓 ∼ 𝒩(𝜇𝑋, 𝜎𝑋 2) Monte Carlo sampling: Generate N counterfactual budget proposals:X1 cf, X2 cf, . . .	cache/easat-7442.pdf	txt/easat-7442.txt