ALD-Lab/SURD

Implementation of Causation Entropy from Clarkson Center for Complex Systems Science (C3S2)

Causal Discovery for Time Series

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.

Fast causal discovery library

Reconstructing shared causal drivers from noisy time series

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

Python package for causal discovery based on LiNGAM.

❓y0 (pronounced "why not?") is for causal inference in Python