Computes various metrics to evaluate the difference between the estimated and true causal graph. Designed primarily for assessing the performance of causal discovery algorithms.
Metrics are supplied as a list with three slots: $adj, $dir, and $other.
$adjMetrics applied to the adjacency confusion matrix (see
confusion()).$dirMetrics applied to the conditional orientation confusion matrix (see
confusion()).$otherMetrics applied directly to the adjacency matrices without computing confusion matrices.
Adjacency confusion matrix and conditional orientation confusion matrix only supports
caugi::caugi objects whose edges are restricted to -->, <->, ---, or absence of an edge.
Arguments
- truth
truth caugi::caugi object.
- est
Estimated caugi::caugi object.
- metrics
List of metrics, see details. If
metrics = "all", all available metrics are computed.
Value
A data.frame with one column for each computed metric. Adjacency metrics are prefixed with "adj_", orientation metrics are prefixed with "dir_", other metrics do not get a prefix.
See also
Other metrics:
confusion(),
f1_score(),
false_omission_rate(),
fdr(),
g1_score(),
npv(),
precision(),
recall(),
reexports,
specificity()
Examples
cg1 <- caugi::caugi(A %-->% B + C)
cg2 <- caugi::caugi(B %-->% A + C)
evaluate(cg1, cg2)
#> adj_precision adj_recall adj_specificity adj_false_omission_rate adj_fdr
#> 1 0.5 0.5 0 1 0.5
#> adj_npv adj_f1_score adj_g1_score dir_precision dir_recall dir_specificity
#> 1 0 0.5 0 0 0 0
#> dir_false_omission_rate dir_fdr dir_npv dir_f1_score dir_g1_score shd hd
#> 1 1 1 0 0 0 3 0
#> aid
#> 1 0.6666667
evaluate(
cg1,
cg2,
metrics = list(
adj = c("precision", "recall"),
dir = c("f1_score"),
other = c("shd")
)
)
#> adj_precision adj_recall dir_f1_score shd
#> 1 0.5 0.5 0 3