Explanatory causal effects for model agnostic explanations

• URL: http://arxiv.org/abs/2206.11529v1
• Date: Thu, 23 Jun 2022 08:25:31 GMT
• Title: Explanatory causal effects for model agnostic explanations
• Authors: Jiuyong Li and Ha Xuan Tran and Thuc Duy Le and Lin Liu and Kui Yu and Jixue Liu
• Abstract summary: We study the problem of estimating the contributions of features to the prediction of a specific instance by a machine learning model. A challenge is that most existing causal effects cannot be estimated from data without a known causal graph. We define an explanatory causal effect based on a hypothetical ideal experiment.
• Score: 27.129579550130423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies the problem of estimating the contributions of features to the prediction of a specific instance by a machine learning model and the overall contribution of a feature to the model. The causal effect of a feature (variable) on the predicted outcome reflects the contribution of the feature to a prediction very well. A challenge is that most existing causal effects cannot be estimated from data without a known causal graph. In this paper, we define an explanatory causal effect based on a hypothetical ideal experiment. The definition brings several benefits to model agnostic explanations. First, explanations are transparent and have causal meanings. Second, the explanatory causal effect estimation can be data driven. Third, the causal effects provide both a local explanation for a specific prediction and a global explanation showing the overall importance of a feature in a predictive model. We further propose a method using individual and combined variables based on explanatory causal effects for explanations. We show the definition and the method work with experiments on some real-world data sets.

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