Framework allowing users to easily set up, execute and visualize counterfactual explanation experiments on ML models.
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Updated
Jun 22, 2022 - Jupyter Notebook
Framework allowing users to easily set up, execute and visualize counterfactual explanation experiments on ML models.
Robust Bayesian Recourse: a robust model-agnostic algorithmic recourse method (UAI'22)
Robust Bayesian Recourse: a robust model-agnostic algorithmic recourse method (UAI'22)
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis.
Counterfactual Shapley Additive Explanation: Experiments
Counterfactual SHAP: a framework for counterfactual feature importance
A Julia package for modelling Algorithmic Recourse Dynamics.
CFXplorer generates optimal distance counterfactual explanations for a given machine learning model.
Recourse Explanation Library in JAX
Python implementation of the work "The importance of Time in Causal Algorithmic Recourse"
RootCLAM: On Root Cause Localization and Anomaly Mitigation through Causal Inference (CIKM 2023)
Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.
(Explainable) Algorithmic Recourse with Reinforcement Learning and MCTS (FARE and E-FARE)
Code for the paper "Personalized Algorithmic Recourse with Preference Elicitation"
This is the repository code for IFC1 - A novel algorithm to generate algorithmic recourse keeping in mind user preference
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