g1_score: G1 score

In causalDisco: Tools for Causal Discovery on Observational Data

G1 score

Description

Computes G1 score from two caugi::caugi objects. It converts the caugi::caugi objects to adjacency matrices and computes G1 score defined as 2 \cdot TN/(2 \cdot TN + FN + FP), where TN are true negatives, FP are false positives, and FN are false negatives. If TN + FN + FP = 0, 1 is returned.

Only supports caugi::caugi objects whose edges are restricted to ⁠-->⁠, ⁠<->⁠, ⁠---⁠, or absence of an edge.

Usage

g1_score(truth, est, type = c("adj", "dir"))

Arguments

truth	A caugi::caugi object representing the truth graph.
est	A caugi::caugi object representing the estimated graph.
type	Character string specifying the comparison type: "adj": adjacency comparison. "dir": orientation comparison conditional on shared adjacencies.

Value

A numeric in [0,1].

References

Petersen, Anne Helby, et al. "Causal discovery for observational sciences using supervised machine learning." arXiv preprint arXiv:2202.12813 (2022).

See Also

Other metrics: confusion(), evaluate(), f1_score(), false_omission_rate(), fdr(), npv(), precision(), recall(), reexports, specificity()

Examples

cg1 <- caugi::caugi(A %-->% B + C)
cg2 <- caugi::caugi(B %-->% A + C)
g1_score(cg1, cg2, type = "adj")
g1_score(cg1, cg2, type = "dir")

causalDisco documentation built on April 13, 2026, 5:06 p.m.