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We introduce MCCE: Monte Carlo sampling of valid and realistic CounterfactualExplanations for tabular data, a novel counterfactual explanation method thatgenerates on-manifold, actionable and valid counterfactuals by modeling thejoint distribution of the mutable features given the immutable features and thedecision. Unlike other on-manifold methods that tend to rely on variationalautoencoders and have strict prediction model and data requirements, MCCEhandles any type of prediction model and categorical features with more thantwo levels. MCCE first models the joint distribution of the features and thedecision with an autoregressive generative model where the conditionals areestimated using decision trees. Then, it samples a large set of observationsfrom this model, and finally, it removes the samples that do not obey certaincriteria. We compare MCCE with a range of state-of-the-art on-manifoldcounterfactual methods using four well-known data sets and show that MCCEoutperforms these methods on all common performance metrics and speed. Inparticular, including the decision in the modeling process improves theefficiency of the method substantially.

MCCE: Monte Carlo sampling of realistic counterfactual explanations