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name: learning-structure-and-parameters description: "Guide pgmpy causal discovery, CI tests, structure scores, expert knowledge, and parameter estimation from data." disable-model-invocation: true metadata: disco-role: operating license: MIT

Learning Structure and Parameters

Use this sub-skill when the task is to learn graph structure or fit parameters from tabular data with pgmpy's canonical learning APIs. It covers causal/structure discovery, conditional-independence tests, structure scores, expert-knowledge constraints, and parameter estimators.

Own and reroute

Own these workflows:

• Structure and causal discovery through pgmpy.causal_discovery: PC, HillClimbSearch, GES, ChowLiu, TAN, SP, TOPIC, ANM, IGCI, ExpertKnowledge, and optional expert/LLM-in-the-loop estimators.
• Conditional-independence test selection through pgmpy.ci_tests.
• Score-based structure learning through pgmpy.structure_score.
• Parameter fitting through pgmpy.parameter_estimator: DiscreteMLE, DiscreteBayesianEstimator, DiscreteEM, and LinearGaussianMLE.

Reroute these requests:

• Manual graph/model object construction, CPD authoring, factor objects, and model validation details -> modeling-and-factors.
• Dataset registries, benchmark loading, metrics, and graph-evaluation reports -> data-io-and-evaluation.
• Causal identification, adjustment/frontdoor sets, treatment-effect estimation, and do-effect prediction -> causal-identification-and-effects.

Canonical stance

Prefer the newer canonical packages over legacy pgmpy.estimators examples:

1. Instantiate a discovery estimator from pgmpy.causal_discovery.
2. Call fit(data).
3. Read learned results from causal_graph_ and adjacency_matrix_.
4. If parameters are needed, build or reuse the appropriate model family and call model.fit(data, estimator=...) with an initialized estimator from pgmpy.parameter_estimator.

Only use legacy pgmpy.estimators when the requested class has no canonical replacement or when reproducing an old API-specific workflow is explicitly required.

First actions for an agent

1. Identify the data type and target model family before choosing a method: discrete, continuous/Gaussian, mixed conditional-Gaussian, tree/classifier, or bivariate direction.
2. Read references/discovery-and-learning-api.md for imports, score names, CI-test names, and estimator contracts.
3. Read references/workflows.md for recipes that combine discovery, expert knowledge, and parameter fitting.
4. If the user reports errors or confusing output, read references/troubleshooting.md before changing code.
5. For a no-network local smoke check, run scripts/learn_structure_smoke.py with the Python environment where pgmpy is installed.

Minimal working pattern

from pgmpy.causal_discovery import HillClimbSearch
from pgmpy.models import DiscreteBayesianNetwork
from pgmpy.parameter_estimator import DiscreteBayesianEstimator

est = HillClimbSearch(scoring_method="bic-d", return_type="dag", show_progress=False).fit(data)
model = DiscreteBayesianNetwork(est.causal_graph_.edges())
model.add_nodes_from(data.columns)
model.fit(data, estimator=DiscreteBayesianEstimator(prior_type="BDeu", equivalent_sample_size=5))

Keep show_progress=False in automated smoke tests and small examples unless the user specifically wants progress bars.