Counterfactual SHAP: a framework for counterfactual feature importance
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Updated
Jul 6, 2023 - HTML
Counterfactual SHAP: a framework for counterfactual feature importance
Counterfactual Shapley Additive Explanation: Experiments
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis.
CFXplorer generates optimal distance counterfactual explanations for a given machine learning model.
Recourse Explanation Library in JAX
Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)
Python implementation of the work "The importance of Time in Causal Algorithmic Recourse"
Code for the paper "Personalized Algorithmic Recourse with Preference Elicitation"
Robust Bayesian Recourse: a robust model-agnostic algorithmic recourse method (UAI'22)
Framework allowing users to easily set up, execute and visualize counterfactual explanation experiments on ML models.
A Julia package for modelling Algorithmic Recourse Dynamics.
(Explainable) Algorithmic Recourse with Reinforcement Learning and MCTS (FARE and E-FARE)
This is the repository code for IFC1 - A novel algorithm to generate algorithmic recourse keeping in mind user preference
Robust Bayesian Recourse: a robust model-agnostic algorithmic recourse method (UAI'22)
RootCLAM: On Root Cause Localization and Anomaly Mitigation through Causal Inference (CIKM 2023)
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