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G1 score

Source: R/metrics.R
g1_score.Rd

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 truth negatives, FP are false positives, and FN are false negatives. If TN + FN + FP = 0, 1 is returned. Only supports caugi::caugi objects with these edge types present -->, <-->, --- and no 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")
#> [1] 0
g1_score(cg1, cg2, type = "dir")
#> [1] 0