Counterfactual Forecasting of Human Behavior using Generative AI and Causal Graphs
结合生成式人工智能与因果图的用户行为反事实预测
This study presents a novel framework for counterfactual user behavior forecasting that combines structural causal models with transformer-based generative artificial intelligence. To model fictitious situations, the method creates causal graphs that map the connections between user interactions, adoption metrics, and product features. The framework generates realistic behavioral trajectories under counterfactual conditions by using generative models that are conditioned on…