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bnlearn is Python package for causal discovery by learning the graphical structure of Bayesian networks, parameter learning, inference, and sampling methods. Because probabilistic graphical models can be difficult to use, Bnlearn contains the most-wanted pipelines. Navigate to API documentations for more detailed information. ⭐️ Star it if you like it ⭐️

Key Features

Feature Description Medium Gumroad/Podcast
Causal Discovery - Overview and Starters Guide Learn the basics of causal modelling. link ---
Structure Learning Learn the model structure from data or with expert knowledge. link Podcast
Causal Predictions Learn to make causal predictions. link ---
Parameter Learning Estimate model parameters (e.g., conditional probability distributions) from observed data. link Podcast
Causal Inference Compute interventional and counterfactual distributions using do-calculus. link Podcast
Generate Synthetic Data Generate synthetic data. link Podcast
Discretize Data Discretize continuous datasets. --- ---
Data Types Discrete, continuous and mixed datasets. --- ---
Comparisons Comparison with other causal libraries. link Podcast

Resources and Links

The following functions are available after installation:

Feature Description
Key Pipelines
Structure learning bn.structure_learning.fit()
Parameter learning bn.parameter_learning.fit()
Inference bn.inference.fit()
Make predictions bn.predict()
Generate Synthetic Data bn.sampling()
Compute Edge Strength bn.independence_test()
Key Functions
Imputations bn.knn_imputer()
Discretizing bn.discretize()
Check Model Parameters bn.check_model()
Create DAG bn.make_DAG()
Get Node Properties bn.get_node_properties()
Get Edge Properties bn.get_edge_properties()
Get Parents From Edges bn.get_parents()
Generate Default CPT per Node bn.generate_cpt()
Generate Default CPTs for All Edges bn.build_cpts_from_structure()
Make Plots
Plotting bn.plot()
Plot Graphviz bn.plot_graphviz()
Compare 2 Networks bn.compare_networks()
Load DAG (bif files) bn.import_DAG()
Load Examples bn.import_example()
Transformation Functions
Convert DAG to Undirected bn.to_undirected()
Convert to one-hot bn.df2onehot()
Convert Adjacency Matrix to Vector bn.adjmat2vec()
Convert Adjacency Matrix to Dictionary bn.adjmat2dict()
Convert Vector to Adjacency Matrix bn.vec2adjmat()
Convert DAG to Adjacency Matrix bn.dag2adjmat()
Convert DataFrame to Onehot bn.df2onehot()
Convert Query to DataFrame bn.query2df()
Convert Vector to DataFrame bn.vec2df()
Metrics
Compute Topological Ordering bn.topological_sort()
Compute Structure Scores bn.structure_scores()
General
Save Model bn.save()
Load Model bn.load()
Print CPTs bn.print_CPD()

Installation

Python 3.10 or newer is required.

Installation: uv-pypi
uv venv
.venv\Scripts\activate
uv pip install bnlearn
Installation: uv-local
uv venv
.venv\Scripts\activate
git clone https://github.com/erdogant/bnlearn.git
cd bnlearn
uv pip install -U -e .
Installation: conda-pypi
conda create -n env_bnlearn python=3.12
conda activate env_bnlearn
pip install bnlearn
Installation: GitHub source
pip install git+https://github.com/erdogant/bnlearn
uv pip install git+https://github.com/erdogant/bnlearn
Run tests (uv)
uv pip install -e ".[dev]"
uv run pytest
Run tests (pip)
pip install -e ".[dev]"
pytest -v

Installation of bnlearn skills

Installation of bnlearn skills for various harnesses. Default is .claude

Install skill (default claude)
bnlearn skill install
Install skill: harnass options {claude, opencode agents or any other name}
bnlearn skill install --harness opencode

Load library

# Import library
import bnlearn as bn

Code Examples

    import bnlearn as bn
    # Example dataframe sprinkler_data.csv can be loaded with: 
    df = bn.import_example()
    # df = pd.read_csv('sprinkler_data.csv')

Cloudy  Sprinkler  Rain  Wet_Grass
0         0          1     0          1
1         1          1     1          1
2         1          0     1          1
3         0          0     1          1
4         1          0     1          1
..      ...        ...   ...        ...
995       0          0     0          0
996       1          0     0          0
997       0          0     1          0
998       1          1     0          1
999       1          0     1          1


    model = bn.structure_learning.fit(df)
    # Compute edge strength with the chi-square test statistic
    model = bn.independence_test(model, df)
    G = bn.plot(model)


# Example: Structure Learning

    model_hc_bic  = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
    model_hc_k2   = bn.structure_learning.fit(df, methodtype='hc', scoretype='k2')
    model_hc_bdeu = bn.structure_learning.fit(df, methodtype='hc', scoretype='bdeu')
    model_ex_bic  = bn.structure_learning.fit(df, methodtype='ex', scoretype='bic')
    model_ex_k2   = bn.structure_learning.fit(df, methodtype='ex', scoretype='k2')
    model_ex_bdeu = bn.structure_learning.fit(df, methodtype='ex', scoretype='bdeu')
    model_cl      = bn.structure_learning.fit(df, methodtype='cl', root_node='Wet_Grass')
    model_tan     = bn.structure_learning.fit(df, methodtype='tan', root_node='Wet_Grass', class_node='Rain')

# Example: Parameter Learning

    import bnlearn as bn
    # Import dataframe
    df = bn.import_example()
    # As an example we set the CPD at False which returns an "empty" DAG
    model = bn.import_DAG('sprinkler', CPD=False)
    # Now we learn the parameters of the DAG using the df
    model_update = bn.parameter_learning.fit(model, df)
    # Make plot
    G = bn.plot(model_update)

# Example: Inference

    import bnlearn as bn
    model = bn.import_DAG('sprinkler')
    query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1,'Sprinkler':0, 'Wet_Grass':1})
    print(query)
    print(query.df)
    
    # Lets try another inference
    query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1})
    print(query)
    print(query.df)

Star history

Star History Chart

Contributors

Thank the contributors!

Maintainer

  • Erdogan Taskesen, github: erdogant
  • Contributions are welcome.
  • Yes! This library is entirely free but it runs on coffee! :) Feel free to support with a Coffee.

Buy me a coffee

Project links

Data verified by PyPI on Aug 29, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Latest release

1 maintainer

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Avatar for erdogant from gravatar.com erdogant

Credits

Author: Erdogan Taskesen

GitHub Statistics

Data verified by PyPI on Aug 29, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

dev

Tags

Python causal discovery causal learning

Classifiers

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