Explanatory causal effects for model agnostic explanations

TL;DR

This paper introduces a new causal effect-based approach for model-agnostic explanations, enabling transparent, data-driven local and global feature importance assessments in machine learning models.

Contribution

It defines an explanatory causal effect based on hypothetical experiments, providing a causal, transparent, and data-driven method for model explanations.

Findings

Method works with real-world datasets

Provides both local and global explanations

Enhances interpretability with causal meaning

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.