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.

Counterfactual SHAP: a framework for counterfactual feature importance

RootCLAM: On Root Cause Localization and Anomaly Mitigation through Causal Inference (CIKM 2023)

Counterfactual Shapley Additive Explanation: Experiments

Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis.

A Julia package for modelling Algorithmic Recourse Dynamics.

CFXplorer generates optimal distance counterfactual explanations for a given machine learning model.

Robust Bayesian Recourse: a robust model-agnostic algorithmic recourse method (UAI'22)

(Explainable) Algorithmic Recourse with Reinforcement Learning and MCTS (FARE and E-FARE)

Robust Bayesian Recourse: a robust model-agnostic algorithmic recourse method (UAI'22)

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"

Recourse Explanation Library in JAX

Original codebase for the paper "Time Can Invalidate Algorithmic Recourse"

Framework allowing users to easily set up, execute and visualize counterfactual explanation experiments on ML models.

This is the repository code for IFC1 - A novel algorithm to generate algorithmic recourse keeping in mind user preference