pgmpy/pgmpy

Health v2 · maintenance only

Adoption not part of the score

3318 stars · 1148 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

pgmpy is a Python library for causal and probabilistic reasoning with graphical models such as Bayesian Networks, Dynamic Bayesian Networks, DAGs, and Structural Equation Models. It provides a unified, scikit-learn compatible API covering causal discovery, parameter estimation, probabilistic and causal inference, causal identification, model validation, and data simulation.

Use cases

• learn causal graph structure from data
• run probabilistic inference on a bayesian network
• estimate causal effects from observational data
• fit conditional probability distributions to a known graph
• simulate synthetic data from a fitted model
• check whether a causal effect is identifiable from a causal graph
• build a probabilistic graphical model in python

When to choose

• you need causal discovery, identification, or effect estimation in Python
• you want exact or approximate inference over Bayesian Networks
• you want sklearn-compatible estimators that fit into ML pipelines
• you need to simulate observational or interventional data from graphical models

When to avoid

• you need deep-learning-based probabilistic modeling or neural networks
• you need a GUI for building Bayesian networks
• you only need general-purpose graph algorithms without probabilistic semantics

Facets

library · maturity stable

machine-learning data-science simulation data-generation sdk machine-learning data-science python cross-platform bayesian-networks causal-inference causal-discovery probabilistic-graphical-models structure-learning parameter-estimation scikit-learn-compatible graphical-models algorithms statistics

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