bipartite {cbl} | R Documentation |
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)
[Package cbl version 0.1.3 Index]