Counterfactual Forecasting of Human Behavior using Generative AI and Causal Graphs
Dharmateja Priyadarshi Uddandarao, Ravi Kiran Vadlamani
这项研究提出了反事实用户行为预测的新框架,将结构因果模型与基于变压器的生成式人工智能相结合。 为了对虚构的情况进行建模,该方法创建了因果图,绘制了用户交互、采用指标和产品功能之间的联系。 该框架通过使用以因果变量为条件的生成模型,在反事实条件下生成现实的行为轨迹。 在来自Web交互,移动应用程序和电子商务的数据集上进行测试,该方法优于传统的预测和提升建模技术。 产品团队可以在部署前有效地模拟和评估可能的干预措施,这要归功于该框架通过因果路径可视化提高了可解释性。
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 causal variables. Tes...