---

name: causalnex description: "Use CausalNex to learn causal structures, fit Bayesian networks, discretize features, generate synthetic graph data, and run inference, evaluation, and latent-variable workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

CausalNex

Use this skill when the task names CausalNex, NOTEARS, DYNOTEARS, BayesianNetwork, InferenceEngine, Discretiser, DAGClassifier, DAGRegressor, BayesianNetworkClassifier, causal DAG learning, or Bayesian-network workflows from this package.

Route by task

• Structure learning from tabular or time-series data -> sub-skills/structure-learning/SKILL.md
• Bayesian-network fitting, inference, metrics, plots, and latent-variable EM -> sub-skills/bayesian-networks/SKILL.md
• Discretizing continuous features, including tree-based or MDLP splits -> sub-skills/discretization/SKILL.md
• Synthetic DAG/data generation, dynamic transforms, and categorical mapping -> sub-skills/synthetic-data/SKILL.md
• Contrib-area or contribution-layout questions -> references/contribution-mirror.md

Fast start

1. Install the package with pip install causalnex.
2. If you need the optional discretizer extras, install pip install "causalnex[all]" or pip install mdlp-discretization~=0.3.3.
3. Run scripts/check_install.py to confirm the core imports and optional backend availability.
4. Read references/installation.md for supported Python and dependency notes.
5. Read references/api-reference.md when you need verified public constructors, signatures, and return-shape notes.
6. Read references/workflows.md for the shortest path into each workflow.
7. Read references/contribution-mirror.md only when the user asks about causalnex.contrib or contribution layout.
8. Read references/troubleshooting.md when imports, optional dependencies, or data-shape checks fail.
9. Read references/repo-provenance.md when you need to compare this skill with the current source revision.

Shared scripts

• scripts/check_install.py verifies the public package imports, torch availability, and the optional MDLP dependency.
• scripts/smoke_structure_learning.py exercises from_pandas, from_numpy, from_pandas_dynamic, DAGClassifier, and DAGRegressor on tiny inputs.
• scripts/smoke_bayesian_network.py fits a tiny Bayesian network, queries marginals, evaluates metrics, and optionally exercises latent-variable EM.
• scripts/smoke_discretizer.py exercises the fixed, uniform, quantile, outlier, percentiles, tree, and MDLP discretizers.
• scripts/smoke_synthetic_data.py exercises the DAG/data generators, dynamic transforms, and categorical mapping helpers.

References

• references/installation.md
• references/api-reference.md
• references/workflows.md
• references/contribution-mirror.md
• references/troubleshooting.md
• references/repo-provenance.md
• references/repo-routing-metadata.json

Avoid

• Do not assume CUDA is required; use_gpu=True falls back to CPU when CUDA is unavailable.
• Do not assume mdlp-discretization is installed unless you asked for the optional discretizer path.
• Do not rely on the original repository checkout; this skill should stand on its bundled references and scripts.