CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows

• URL: http://arxiv.org/abs/2303.14668v1
• Date: Sun, 26 Mar 2023 09:51:04 GMT
• Title: CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows
• Authors: Tri Dung Duong, Qian Li, Guandong Xu
• Abstract summary: Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. State-of-the-art counterfactual explanation methods are proposed to use variational autoencoder (VAE) to achieve promising improvements. We design a robust and efficient counterfactual explanation framework, namely CeFlow, which utilizes normalizing flows for the mixed-type of continuous and categorical features.
• Score: 11.108866104714627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as instructions to guide end users on how to observe the desired results by altering samples. Although state-of-the-art counterfactual explanation methods are proposed to use variational autoencoder (VAE) to achieve promising improvements, they suffer from two major limitations: 1) the counterfactuals generation is prohibitively slow, which prevents algorithms from being deployed in interactive environments; 2) the counterfactual explanation algorithms produce unstable results due to the randomness in the sampling procedure of variational autoencoder. In this work, to address the above limitations, we design a robust and efficient counterfactual explanation framework, namely CeFlow, which utilizes normalizing flows for the mixed-type of continuous and categorical features. Numerical experiments demonstrate that our technique compares favorably to state-of-the-art methods. We release our source at https://github.com/tridungduong16/fairCE.git for reproducing the results.

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