#Causal-models
Showing 10 of 10 repositories tagged #causal-models, ranked by stars
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 Toolkit for Causal and Probabilistic Reasoning
A Python library that helps data scientists to infer causation rather than observing correlation.
A Python package for modular causal inference analysis and model evaluations
Must-read papers and resources related to causal inference and machine (deep) learning
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
Python package for causal discovery based on LiNGAM.
A resource list for causality in statistics, data science and physics
Streamline a data analysis process
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