RecourseBench documentation ============================ **RecourseBench** is a modular framework for reproducible algorithmic recourse evaluation. It composes five extensible component types — datasets, preprocessing steps, target models, recourse methods, and evaluation metrics — into experiments that are configured as data and run end to end. Live example ------------ Edit the factual case and select a recourse method to see the kind of result RecourseBench evaluates: a counterfactual that changes the model prediction with a small set of feature edits. .. raw:: html

Toy credit recourse run

Editable applicant, same toy model, different recourse method.
Factual case
Prediction
Counterfactual result
validity
L1 distance
features changed
.. grid:: 1 2 2 2 :gutter: 3 .. grid-item-card:: Getting started :link: getting_started :link-type: doc Install the package, run your first experiment from a YAML config, and use the Python API. .. grid-item-card:: API reference :link: reference/index :link-type: doc Full description of every public class and function: arguments, return values, and behaviour. .. grid-item-card:: Methods :link: methods :link-type: doc Browse all registered recourse methods and open the RecourseBench paper. .. grid-item-card:: Extending the framework :link: extending :link-type: doc Register new datasets, preprocessing, models, methods, and metrics. .. grid-item-card:: Agent tools :link: agent_tools :link-type: doc Use the RecourseBench skills and MCP server from coding agents without giving up reproducibility or safety. .. toctree:: :maxdepth: 2 :hidden: getting_started methods extending agent_tools reference/index