Analytics
- CausalDisco.analytics.order_alignment(W, scores, tol=0.0)[source]
Computes a measure of the agreement between a causal ordering following the topology of the (weighted) adjacency matrix W and an ordering by the scores.
- Parameters:
W – Weighted/Binary DAG adjacency matrix (\(d \times d\) np.array).
scores – Vector of scores (np.array with \(d\) entries).
tol (optional) – Tolerance threshold for score comparisons (non-negative float).
- Returns:
Scalar measure of agreement between the orderings.
- CausalDisco.analytics.r2_sortability(X, W, tol=0.0)[source]
Sortability by \(R^2\).
- Parameters:
X – Data (\(n \times d\) np.array).
W – Weighted/Binary ground-truth DAG adjacency matrix (\(d \times d\) np.array).
- Returns:
\(R^2\)-sortability value (\(\in [0, 1]\)) of the data
- CausalDisco.analytics.r2coeff(X)[source]
Compute the \(R^2\) of each variable using partial correlations obtained through matrix inversion.
- Parameters:
X – Data (\(d \times n\) np.array - note that the dimensions here are different from other methods, following np.corrcoef).
- Returns:
Array of \(R^2\) values for all variables.
- CausalDisco.analytics.snr_sortability(X, W, tol=0.0)[source]
Sortability by signal-to-noise (SnR) ratio (also referred to as cause-explained variance CEV).
- Parameters:
X – Data (\(n \times d\) np.array).
W – Weighted/Binary ground-truth DAG adjacency matrix (\(d \times d\) np.array).
- Returns:
math: SnR-sortability value (\(\in [0, 1]\)) of the data