Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)
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Updated
Mar 13, 2023 - HTML
Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)
Python implementation of the work "The importance of Time in Causal Algorithmic Recourse"
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.
Code for the paper "Personalized Algorithmic Recourse with Preference Elicitation"
A Julia package for modelling Algorithmic Recourse Dynamics.
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)
(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
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
Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
Counterfactual Shapley Additive Explanation: Experiments
Counterfactual SHAP: a framework for counterfactual feature importance
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