interpretml/DiCE

Health v2 · maintenance only

Adoption not part of the score

1525 stars · 235 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DiCE is a Python library that generates diverse counterfactual explanations for any machine learning model, showing feature-perturbed versions of an input that would flip the model's decision. It supports model-agnostic methods (random sampling, genetic search, KD-tree) as well as gradient-based methods for TensorFlow and PyTorch models.

Use cases

• explain why a loan application was rejected and what would change the outcome
• generate counterfactual examples for a scikit-learn classifier
• produce what-if explanations for a deep learning model
• audit model decisions in finance or healthcare with actionable explanations
• generate diverse counterfactuals for a black-box ML model
• complement SHAP or LIME explanations with counterfactuals

When to choose

• you need actionable 'what-if' explanations for model predictions
• you want model-agnostic counterfactual generation across sklearn, TensorFlow, or PyTorch models
• you need multiple diverse counterfactuals rather than a single nearest example
• you are doing explainable AI research or building XAI pipelines in Python

When to avoid

• you need feature-importance or attribution explanations rather than counterfactuals
• you work outside Python or need a GUI-based explanation tool
• your model is a simple linear model where counterfactuals add little value
• you need real-time explanations with strict latency constraints

Facets

library · maturity active

machine-learning nlp machine-learning artificial-intelligence data-science python counterfactual-explanations explainable-ai xai interpretable-ml model-agnostic what-if-explanations

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