论文标题
从统计到因果学习
From Statistical to Causal Learning
论文作者
论文摘要
我们描述了研究基本思想，以建立和理解人为智能的系统：从统计学习的符号方法到依赖因果关系概念的介入模型。机器学习和AI的一些硬开放问题与因果关系本质上相关，并且进步可能需要我们对如何对数据建模和推断因果关系的理解的进步。
We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models relying on concepts of causality. Some of the hard open problems of machine learning and AI are intrinsically related to causality, and progress may require advances in our understanding of how to model and infer causality from data.
