VICE

A game theoretic approach to explain the output of any machine learning model.

Bayesian Modeling and Probabilistic Programming in Python

Deep universal probabilistic programming with Python and PyTorch

? Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Algorithms for explaining machine learning models

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

Fast and Easy Infinite Neural Networks in Python

A Python library that helps data scientists to infer causation rather than observing correlation.

A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)