Causal Interpretations of Black-Box Models
探讨如何从黑箱机器学习模型中提取因果解释，回顾因果推断中的语言和概念，提出三个必要条件：良好预测性能、因果图形式的领域知识和合适的可视化工具，并通过实例展示潜在因果关系。
The fields of machine learning and causal inference have developed many concepts, tools, and theory that are potentially useful for each other. Through exploring the possibility of extracting causal interpretations from black-box machine-trained models, we briefly review the languages and concepts in causal inference that may be interesting to machine learning researchers. We start with the curious observation that Friedman's partial dependence plot has exactly the same formula as Pearl's back-door adjustment and discuss three requirements to make causal interpretations: a model with good predictive performance, some domain knowledge in the form of a causal diagram and suitable visualization tools. We provide several illustrative examples and find some interesting and potentially causal relations using visualization tools for black-box models.