algorithmic-recourse
There are 17 repositories under algorithmic-recourse topic.
charmlab/recourse
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
Networks-Learning/strategic-decisions
Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.
jpmorganchase/cf-shap
Counterfactual SHAP: a framework for counterfactual feature importance
hanxiao0607/RootCLAM
RootCLAM: On Root Cause Localization and Anomaly Mitigation through Causal Inference (CIKM 2023)
jpmorganchase/cf-shap-facct22
Counterfactual Shapley Additive Explanation: Experiments
unitn-sml/syn-interventions-algorithmic-recourse
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis.
JuliaTrustworthyAI/AlgorithmicRecourseDynamics.jl
A Julia package for modelling Algorithmic Recourse Dynamics.
kyosek/CFXplorer
CFXplorer generates optimal distance counterfactual explanations for a given machine learning model.
duynht/robust-bayesian-recourse
Robust Bayesian Recourse: a robust model-agnostic algorithmic recourse method (UAI'22)
unitn-sml/recourse-fare
(Explainable) Algorithmic Recourse with Reinforcement Learning and MCTS (FARE and E-FARE)
VinAIResearch/robust-bayesian-recourse
Robust Bayesian Recourse: a robust model-agnostic algorithmic recourse method (UAI'22)
BirkhoffG/ReLax
Recourse Explanation Library in JAX
marti5ini/time-car
Python implementation of the work "The importance of Time in Causal Algorithmic Recourse"
pat-alt/endogenous-macrodynamics-in-algorithmic-recourse
Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)
unitn-sml/pear-personalized-algorithmic-recourse
Code for the paper "Personalized Algorithmic Recourse with Preference Elicitation"
cpdl1997/ifc1_algorithm
This is the repository code for IFC1 - A novel algorithm to generate algorithmic recourse keeping in mind user preference
drobiu/recourse_analysis
Framework allowing users to easily set up, execute and visualize counterfactual explanation experiments on ML models.