shap and cf-shap
B extends A by adding counterfactual reasoning to SHAP's feature importance explanations, making them complements rather than competitors—you would use B's counterfactual framework on top of A's core Shapley value implementation.
About shap
shap/shap
A game theoretic approach to explain the output of any machine learning model.
Based on the README, here's a technical summary: Implements fast exact algorithms for tree ensemble models (XGBoost, LightGBM, CatBoost, scikit-learn, PySpark) via optimized C++ backends, alongside approximation methods for deep learning (DeepExplainer leveraging DeepLIFT) and NLP transformers using coalitional game rules. Provides multiple visualization outputs—waterfall plots, force plots, dependence scatter plots, and beeswarm distributions—to show feature contributions at instance and global levels. Integrates directly with popular ML frameworks and Hugging Face transformers, supporting both tabular and text-based model explanations.
About cf-shap
jpmorganchase/cf-shap
Counterfactual SHAP: a framework for counterfactual feature importance
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