Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation

• URL: http://arxiv.org/abs/2003.03934v3
• Date: Thu, 19 Mar 2020 20:23:46 GMT
• Title: Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
• Authors: Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu
• Abstract summary: Most machine learning models are black-boxes, and it is obscure how the decisions are made by them. To provide insights into the decision making processes of these models, a variety of traditional interpretable models have been proposed. In this work, models that aim to answer causal questions are referred to as causal interpretable models.
• Score: 30.664546254344593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made by them. This makes the models unreliable and untrustworthy. To provide insights into the decision making processes of these models, a variety of traditional interpretable models have been proposed. Moreover, to generate more human-friendly explanations, recent work on interpretability tries to answer questions related to causality such as "Why does this model makes such decisions?" or "Was it a specific feature that caused the decision made by the model?". In this work, models that aim to answer causal questions are referred to as causal interpretable models. The existing surveys have covered concepts and methodologies of traditional interpretability. In this work, we present a comprehensive survey on causal interpretable models from the aspects of the problems and methods. In addition, this survey provides in-depth insights into the existing evaluation metrics for measuring interpretability, which can help practitioners understand for what scenarios each evaluation metric is suitable.

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