cbl (version 0.1.3)

bipartite: Simulated data

Description

Simulated dataset of \(n=200\) samples with 2 foreground variables and 10 background variables. The design follows that of Watson & Silva (2022), with \(Z\) drawn from a multivariate Gaussian distribution with a Toeplitz covariance matrix of autocorrelation \(\rho = 0.25\). Expected sparsity is 0.5, signal-to-noise ratio is 2, and structural equations are linear. The ground truth for foreground variables is \(X \rightarrow Y\).

Usage

data(bipartite)

Format

A list with two elements: x (foreground variables), and z (background variables).

References

Watson, D.S. & Silva, R. (2022). Causal discovery under a confounder blanket. To appear in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence. arXiv preprint, 2205.05715.

Examples

# Load data
data(bipartite)
x <- bipartite$x
z <- bipartite$z

# Set seed
set.seed(42)

# Run CBL
cbl(x, z)