AdjacencyConfusionMatrix

class pgmpy.metrics.AdjacencyConfusionMatrix(metrics: list[str] | None = None)

Bases: _BaseSupervisedMetric

Computes confusion matrix based metrics for comparing causal graph skeletons.

Treats edge presence/absence in the undirected skeleton as a binary classification problem and computes confusion matrix based metrics.

Parameters:

metricsList[str], optional

List of metrics to compute. If None, computes all available metrics.

cm : Confusion matrix for skeleton edge presence. precision : Fraction of estimated skeleton edges that are correct (TP / (TP + FP)). recall : Fraction of true skeleton edges that are recovered (TP / (TP + FN)). f1 : Harmonic mean of precision and recall. npv : Fraction of absent estimated edges that are truly absent (TN / (TN + FN)). specificity : Fraction of truly absent edges correctly predicted absent (TN / (TN + FP)).

Returns:

Dict[str, float]

Dictionary containing computed metrics.

References

[1]

Petersen, A. H. (2025). Are you doing better than random guessing? a call for using negative controls when evaluating causal discovery algorithms. Proceedings of the Forty-First Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil: JMLR.org. https://arxiv.org/abs/2412.10039

Examples

>>> from pgmpy.metrics import AdjacencyConfusionMatrix
>>> from pgmpy.base import DAG
>>> true_dag = DAG(
...     [
...         ("Smoking", "Lung_Cancer"),
...         ("Smoking", "Heart_Disease"),
...         ("Age", "Heart_Disease"),
...         ("Age", "Lung_Cancer"),
...     ]
... )
>>> est_dag = DAG([("Smoking", "Lung_Cancer"), ("Age", "Heart_Disease")])
>>> cm = AdjacencyConfusionMatrix()
>>> result = cm.evaluate(true_dag, est_dag)
>>> result["precision"]
1.0
>>> result["recall"]
0.5
>>> result["cm"]
Estimated       Est Present  Est Absent
Actual
Actual Present            2           2
Actual Absent             0           2

Compute only selected metrics:

>>> cm = AdjacencyConfusionMatrix(metrics=["precision", "recall", "f1"])
>>> result = cm.evaluate(true_dag, est_dag)
>>> "f1" in result
True
>>> "npv" in result
False