用生成AI和因果图预测用户行为变化，帮产品提前试错。
Counterfactual Forecasting of Human Behavior using Generative AI and Causal Graphs
- 结合因果图与生成模型，模拟假设情境下的用户行为
- 在多场景数据上优于传统预测方法，可生成真实轨迹
- 可视化因果路径，适合产品决策与干预评估
本研究提出一种新框架，通过结构化因果模型与基于Transformer的生成式人工智能，实现对反事实用户行为的预测。该方法构建用户交互、采纳指标与产品功能间的因果图，利用条件于因果变量的生成模型，在虚构条件下生成合理的用户行为轨迹。在网页交互、移动应用和电子商务数据集上测试，其性能超越传统预测与提升建模技术。借助因果路径可视化，该框架提升了可解释性，使产品团队能在部署前有效模拟并评估潜在干预措施的效果。
原文摘要 · Abstract (English)
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. Tested on datasets from web interactions, mobile applications, and e-commerce, the methodology outperforms conventional forecasting and uplift modeling techniques. Product teams can effectively simulate and assess possible interventions prior to deployment thanks to the framework improved interpretability through causal path visualization.
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