Methods

RecourseBench integrates a broad set of algorithmic recourse methods behind a single MethodObject interface. The links below point to the original method papers identified from the RecourseBench paper and its per-method reproduction notes.

Registry name	Method	Paper
apas	APAS	Probabilistically Robust Counterfactual Explanations under Model Changes
arg_ensembling	Argumentative Ensembles	Argumentative Ensembling for Robust Recourse under Model Multiplicity
cchvae	C-CHVAE	Learning Model-Agnostic Counterfactual Explanations for Tabular Data
cemsp	CEMSP	Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations
cfrl	CFRL	Model-Agnostic and Scalable Counterfactual Explanations via Reinforcement Learning
cfvae	CF-VAE	Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
claproar	CLAPROAR	Endogenous Macrodynamics in Algorithmic Recourse
clue	CLUE	Getting a CLUE: A Method for Explaining Uncertainty Estimates
cogs	COGS	Robustness of Recourse Methods
cols	COLS	Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions
cruds	CRUDS	CRUDS: Counterfactual Recourse Using Disentangled Subspaces
cvas_proj	CVAS-PROJ	Coverage-Validity-Aware Algorithmic Recourse
dice	DiCE	Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
dice_repro	DiCE Reproduction	Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
diverse_dist	Diverse Dist	Promoting Counterfactual Robustness through Diversity
face	FACE	FACE: Feasible and Actionable Counterfactual Explanations
feature_tweak	Feature Tweak	Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
gravitational	Gravitational	Endogenous Macrodynamics in Algorithmic Recourse
gs	Growing Spheres	Inverse Classification for Comparison-based Interpretability in Machine Learning
larr	LARR	Learning-Augmented Robust Algorithmic Recourse
mace	MACE	Model-Agnostic Counterfactual Explanations for Consequential Decisions
probe	PROBE	Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
proplace	PROPLACE	Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
rbr	RBR	Robust Bayesian Recourse
revise	REVISE	Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
roar	ROAR	Towards Robust and Reliable Algorithmic Recourse
sns	SNS	Consistent Counterfactuals for Deep Models
toy	Toy	RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation
trex	T-REX	Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
wachter	Wachter	Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

Source

The mappings above follow the method names and citations used in the RecourseBench paper:

• RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation