MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation

• URL: http://arxiv.org/abs/2205.15540v1
• Date: Tue, 31 May 2022 04:57:06 GMT
• Title: MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation
• Authors: Wenzhuo Yang and Jia Li and Caiming Xiong and Steven C.H. Hoi
• Abstract summary: We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
• Score: 132.77005365032468
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods often assume that the underlying machine-learning model is differentiable and treat categorical attributes as continuous ones, which restricts their real-world applications when categorical attributes have many different values or the model is non-differentiable. To make counterfactual explanation suitable for real-world applications, we propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE), which adopts a newly designed pipeline that can efficiently handle non-differentiable machine-learning models on a large number of feature values. in our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.

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