Papers › VCNet: A self-explaining model for realistic counterfactual generation

VCNet: A self-explaining model for realistic counterfactual generation

21 Dec 2022arXiv:2212.10847archive 2025-07-28

Victor Guyomard, Françoise Fessant, Thomas Guyet, Tassadit Bouadi, Alexandre Termier

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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cat_normalize Orange-OpenSource/carla_croco_vcnet/carla/self_explaining_model/catalog/vcnet/library/utils.py official repository unverified MIT (permissive) · 5ad5d0944ec31447 · report
int_round_dataset Orange-OpenSource/carla_croco_vcnet/carla/self_explaining_model/catalog/vcnet/library/utils.py official repository unverified MIT (permissive) · 8b3abecc77b71942 · report
load_json Orange-OpenSource/carla_croco_vcnet/carla/self_explaining_model/catalog/vcnet/library/utils.py official repository unverified MIT (permissive) · 934c3e9b8141d431 · report
perturb_sample Orange-OpenSource/carla_croco_vcnet/carla/evaluation/robustness_metrics.py official repository unverified MIT (permissive) · 07e37d41ea7bce74 · report
perturb_sample_gaussian Orange-OpenSource/carla_croco_vcnet/carla/evaluation/robustness_metrics.py official repository unverified MIT (permissive) · 6bb4672b81a18f29 · report
perturb_sample_uniform Orange-OpenSource/carla_croco_vcnet/carla/evaluation/robustness_metrics.py official repository unverified MIT (permissive) · 565236516cde7f18 · report
remove_nans Orange-OpenSource/carla_croco_vcnet/carla/evaluation/process_nans.py official repository unverified MIT (permissive) · 94cef7815945f3ca · report
reparametrization_trick Orange-OpenSource/carla_croco_vcnet/carla/recourse_methods/catalog/croco/library/croco.py official repository unverified MIT (permissive) · c37a3f324b04124c · report
reparametrization_trick_gaussian Orange-OpenSource/carla_croco_vcnet/carla/recourse_methods/catalog/croco/library/croco.py official repository unverified MIT (permissive) · 85bc5d90f2a78ca5 · report
reparametrization_trick_uniform Orange-OpenSource/carla_croco_vcnet/carla/recourse_methods/catalog/croco/library/croco.py official repository unverified MIT (permissive) · eb844a32fb77d424 · report

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