---

name: structure-learning description: "Learn causal graph structure with NOTEARS, DYNOTEARS, and the sklearn DAG wrappers, and debug numeric-data and convergence failures." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Structure Learning

Use this sub-skill when you want to learn a causal graph from data, tune NOTEARS/DYNOTEARS, or use the sklearn-style DAGClassifier and DAGRegressor wrappers.

Route here when

• The request names from_pandas, from_numpy, from_pandas_dynamic, NOTEARS, DYNOTEARS, DAGClassifier, DAGRegressor, or NotearsMLP.
• You need to convert numeric tabular data into a StructureModel.
• You need to learn a dynamic causal graph from lagged time-series data.
• You need a sklearn-style wrapper that returns feature importances or graph plots.

Route elsewhere when

• You already have the DAG and need CPDs, inference, intervention, metrics, or EM -> ../bayesian-networks/SKILL.md.
• You need feature discretization before BN fitting -> ../discretization/SKILL.md.
• You need synthetic DAGs or toy time-series data -> ../synthetic-data/SKILL.md.

Start fast

1. Read references/api-reference.md for the verified constructors and key parameters.
2. Read references/workflows.md for the shortest tabular, dynamic, and sklearn wrapper recipes.
3. Run ../../scripts/smoke_structure_learning.py after install changes or API edits.
4. Read references/troubleshooting.md if the learner rejects your data or fails to converge.

Notes

• use_gpu=True is only a preference for PyTorch NOTEARS; the implementation falls back to CPU when CUDA is unavailable.
• The legacy causalnex.structure.notears functions do not take use_gpu; use the PyTorch module or wrapper notears_mlp_kwargs for device controls.
• from_pandas and from_numpy expect numeric input and reject NaN or infinity.
• StructureModel allows cycles, but the learned Bayesian-network path does not.