Generate Diverse Counterfactual Explanations for any machine learning model.
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Updated
Apr 17, 2024 - Python
Generate Diverse Counterfactual Explanations for any machine learning model.
A collection of research materials on explainable AI/ML
Optimal binning: monotonic binning with constraints. Support batch & stream optimal binning. Scorecard modelling and counterfactual explanations.
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Model Agnostic Counterfactual Explanations
CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox
Code accompanying the paper "Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers"
Interpretable Control Exploration and Counterfactual Explanation (ICE) on StyleGAN
A collection of algorithms of counterfactual explanations.
An Open-Source Library for the interpretability of time series classifiers
Counterfactual SHAP: a framework for counterfactual feature importance
Meaningfully debugging model mistakes with conceptual counterfactual explanations. ICML 2022
A package for Counterfactual Explanations and Algorithmic Recourse in Julia.
Counterfactual Shapley Additive Explanation: Experiments
The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era computer vision solutions.
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
Global Counterfactual Explainer for Graph Neural Networks
Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
Introducing the Alien Zoo approach: An experimental framework for evaluating counterfactual explanations for ML
Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.
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