jtextor/dagitty

:arrow_lower_left: :arrow_lower_right: An R package for working with causal directed acyclic graphs (DAGs)

Repository for the Tetrad Project, www.phil.cmu.edu/tetrad.

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

Structural causal models with Bayesian estimation and interventional simulation via a concise DSL.

Causal inference, graphical models and structure learning in Julia

Python Toolkit for Causal and Probabilistic Reasoning

Unified causal discovery engine

Scalable open-source software to run, develop, and benchmark causal discovery algorithms

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.