SGS

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

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

Open-source framework for uncertainty and deep learning models in PyTorch :seedling:

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

Scalable inference for a generative model of astronomical images

A web app to create and browse text visualizations for automated customer listening.