.. pgmpy documentation master file

.. title:: Documentation — pgmpy

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   :description: pgmpy documentation for causal discovery, model testing, causal effect estimation, parameter estimation, probabilistic and causal inference, and simulations in Python.

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         Python toolkit for causal and probabilistic reasoning

      pgmpy is a Python library for causal and probabilistic reasoning with graphical models. It covers the full workflow from learning causal graphs from data to estimating causal effects, running probabilistic inference, and simulating data from fitted models. All algorithms follow a unified, composable API and are scikit-learn compatible where possible, so they work standalone, in sklearn pipelines, or as building blocks for higher-level tools.


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            Getting Started

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            User Guide

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            Example Notebooks

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            API Reference

Key Features
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   .. grid-item-card:: Causal Discovery / Structure Learning
      :link: quickstart-causal-discovery
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      Learn causal graphs from data using scikit-learn compatible implementations.

   .. grid-item-card:: Parameter Estimation
      :link: quickstart-parameter-estimation
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      Estimate conditional distributions for nodes in the model.

   .. grid-item-card:: Probabilistic Inference
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      Compute posterior distributions from the learned model using exact or approximate inference.

   .. grid-item-card:: Causal Identification
      :link: quickstart-causal-identification
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      Given a causal graph determine how to estimate the a causal query.

   .. grid-item-card:: Causal Inference
      :link: quickstart-causal-inference
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      Compute interventional and counterfactual distributions from models.

   .. grid-item-card:: Example Datasets and Models
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      Built-in collection of example Bayesian Networks and datasets from different sources.

   .. grid-item-card:: Simulations
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      Simulate data from models under various scenarios.

   .. grid-item-card:: Extend pgmpy
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      Write your own custom pgmpy plugable methods using our extension templates.


.. toctree::
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   Getting Started <started/index>
   User Guide <documentation>
   Examples <examples>
   API Reference <reference>
   Citation <citation>
   Getting Involved <development>