pgmpy/pgmpy

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.

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

Causal Discovery in Python. Learning causality from data.

Probabilistic reasoning and statistical analysis in TensorFlow

Bayesian Modeling and Probabilistic Programming in Python

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Deep universal probabilistic programming with Python and PyTorch

Uplift modeling and causal inference with machine learning algorithms

Exploratory analysis of Bayesian models with Python