pgmpy
A Python toolkit for causal and probabilistic reasoning using graphical models like Bayesian Networks and Structural Equation Models.
What is pgmpy?
pgmpy is a Python toolkit for causal and probabilistic reasoning using graphical models such as Bayesian Networks, Dynamic Bayesian Networks, and Structural Equation Models. It provides data structures and algorithms for tasks like causal discovery, inference, parameter estimation, and simulations, helping users model and analyze complex probabilistic relationships.
Data scientists, researchers, and machine learning practitioners working on causal inference, probabilistic modeling, or graphical model applications in Python.
Developers choose pgmpy for its comprehensive, modular API that supports a wide range of graphical models and causal reasoning tasks, with scikit-learn compatibility for easy integration into existing machine learning workflows.
Overview
Python Toolkit for Causal and Probabilistic Reasoning
Use Cases
Best For
- Learning causal structures from observational data
- Performing probabilistic inference in Bayesian Networks
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