This is the repository code for IFC1 - A novel algorithm to generate algorithmic recourse keeping in mind user preference
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Updated
May 16, 2024 - Python
This is the repository code for IFC1 - A novel algorithm to generate algorithmic recourse keeping in mind user preference
Code for the paper "Personalized Algorithmic Recourse with Preference Elicitation"
(Explainable) Algorithmic Recourse with Reinforcement Learning and MCTS (FARE and E-FARE)
Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.
RootCLAM: On Root Cause Localization and Anomaly Mitigation through Causal Inference (CIKM 2023)
Python implementation of the work "The importance of Time in Causal Algorithmic Recourse"
Recourse Explanation Library in JAX
CFXplorer generates optimal distance counterfactual explanations for a given machine learning model.
A Julia package for modelling Algorithmic Recourse Dynamics.
Counterfactual SHAP: a framework for counterfactual feature importance
Counterfactual Shapley Additive Explanation: Experiments
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis.
Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
Robust Bayesian Recourse: a robust model-agnostic algorithmic recourse method (UAI'22)
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.
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