bipartite: Simulated data

In cbl: Causal Discovery under a Confounder Blanket

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 ρ = 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)

cbl documentation built on Dec. 28, 2022, 1:55 a.m.