Abstract
Counterfactual explanation is a common class of methods to make local explanations of machine learning decisions. For a given instance, these methods aim to find the smallest modification of feature values that changes the predicted decision made by a machine learning model. One of the challenges of counterfactual explanation is the efficient generation of realistic counterfactuals. To address this challenge, we propose VCNet – Variational Counter Net – a model architecture that combines a predictor and a counterfactual generator that are jointly trained, for regression or classification tasks. VCNet is able to both generate predictions, and to generate counterfactual explanations without having to solve another minimisation problem. Our contribution is the generation of counterfactuals that are close to the distribution of the predicted class. This is done by learning a variational autoencoder conditionally to the output of the predictor in a join-training fashion. We present an empirical evaluation on tabular datasets and across several interpretability metrics. The results are competitive with the state-of-the-art method.
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Notes
- 1.
For Kingma et al. [12], what we call the “content” in this paper is denoted the “style”. It refers to the writing style of digits in MNIST-like datasets.
- 2.
By Self-explainable model here we mean that the predictor is constrained by the counterfactual generator during training but the explanation is not directly used to produce model output as in [1].
- 3.
Note that the quality of the generated counterfactual depends on the quality of the learned latent space.
- 4.
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Guyomard, V., Fessant, F., Guyet, T., Bouadi, T., Termier, A. (2023). VCNet: A Self-explaining Model for Realistic Counterfactual Generation. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham. https://doi.org/10.1007/978-3-031-26387-3_27
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