Linking Model Intervention to Causal Interpretation in Model Explanation

• URL: http://arxiv.org/abs/2410.15648v1
• Date: Mon, 21 Oct 2024 05:16:59 GMT
• Title: Linking Model Intervention to Causal Interpretation in Model Explanation
• Authors: Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu,
• Abstract summary: We will study the conditions when an intuitive model intervention effect has a causal interpretation. This work links the model intervention effect to the causal interpretation of a model. Experiments on semi-synthetic datasets have been conducted to validate theorems and show the potential for using the model intervention effect for model interpretation.
• Score: 34.21877996496178
• License:
• Abstract: Intervention intuition is often used in model explanation where the intervention effect of a feature on the outcome is quantified by the difference of a model prediction when the feature value is changed from the current value to the baseline value. Such a model intervention effect of a feature is inherently association. In this paper, we will study the conditions when an intuitive model intervention effect has a causal interpretation, i.e., when it indicates whether a feature is a direct cause of the outcome. This work links the model intervention effect to the causal interpretation of a model. Such an interpretation capability is important since it indicates whether a machine learning model is trustworthy to domain experts. The conditions also reveal the limitations of using a model intervention effect for causal interpretation in an environment with unobserved features. Experiments on semi-synthetic datasets have been conducted to validate theorems and show the potential for using the model intervention effect for model interpretation.

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