Running causal discovery algorithms
We will for this section use the num_data dataset
included in the package for demonstrating how to run causal discovery
algorithms. It contains 5 numerical variables,
X1, X2, X3, Z, and Y.
data(num_data)
head(num_data)
#> X1 X2 X3 Z Y
#> 1 3.900715 7.048325 6.964806 10.272479 15.35505
#> 2 4.736112 6.236746 5.666022 10.357262 23.36749
#> 3 4.657992 12.169805 9.127046 9.138338 25.32495
#> 4 5.176469 6.392344 6.101088 10.808335 26.75643
#> 5 4.535538 10.305236 7.465185 10.612735 22.67612
#> 6 4.885914 10.018856 7.413312 9.375931 28.03132To make the different causal graphs easier to interpret, we define a custom fixed layout for plotting the results:
plot_layout <- data.frame(
name = c("Z", "X3", "X1", "X2", "Y"),
x = c(0.00, 0.50, 0.00, 0.50, 0.25),
y = c(0.0, 0.0, 0.5, 0.5, 1.0)
)To run a causal discovery algorithm, we first define the algorithm
using the corresponding function, and then pass it to the
disco() function along with the data. Below we demonstrate
this process using the Peter-Clark (PC) algorithm from bnlearn with
Fisher’s Z test:
pc_bnlearn <- pc(
engine = "bnlearn", # Use the bnlearn implementation
test = "fisher_z", # Use Fisher's Z test for conditional independence
alpha = 0.05 # Significance level for the test
)
pc_result_bnlearn <- disco(data = num_data, method = pc_bnlearn)We can visualize the results using plot():
The first notable feature of this plot is that some edges are
directed, while others are undirected. For example, the edge from
X1 to Y is directed, indicating a causal
effect of X1 on Y. In contrast, the edge
between X1 and X3 is undirected, indicating
that the data alone do not provide sufficient information to determine
the causal direction. Both orientations X1 %-->% X3 and
X3 %-->% X1 are compatible with the observed conditional
independencies.
To view all engines available for a specific algorithm, you can see
the documentation using ?pc, where all options are listed
under the engine argument. Instead of using bnlearn, we can
also use the PC implementation from the pcalg package with the same
test:
pc_pcalg <- pc(
engine = "pcalg", # Use the pcalg implementation
test = "fisher_z", # Use Fisher's Z test for conditional independence
alpha = 0.05 # Significance level for the test
)
pc_result_pcalg <- disco(data = num_data, method = pc_pcalg)
plot(pc_result_pcalg, layout = plot_layout, main = "PC Fisher Z (pcalg)")
We see that the results using the PC algorithm implemented in bnlearn and pcalg gives the same output on this dataset.
You can also use a different algorithm altogether, such as the GES algorithm. It follows the same pattern, however GES is a score-based algorithm, so instead of a test and an alpha level, we need to specify a score. Below we will use the Extended Bayesian Information Criterion (EBIC) score from Tetrad.
Tetrad Setup Instructions
causalDisco provides an interface to the Java library Tetrad for causal
discovery algorithms. To use algorithms from Tetrad you need to install
a Java Development Kit (JDK) >= 21. We recommend Eclipse Temurin
(OpenJDK), available at https://adoptium.net/en-GB/temurin/releases. When using
the installer from the Temurin website, make sure to select the option
to set the JAVA_HOME environment variable during
installation, so rJava correctly detects the Java installation.
For a simpler setup, we recommend using the rJavaEnv package, which
provides a convenient function to install Java and configure the
environment automatically for rJava. You can install Java using the
rJavaEnv::java_quick_install() function:
# Use the development version of rJavaEnv from GitHub
# pak::pak("e-kotov/rJavaEnv")
rJavaEnv::java_quick_install(version = 25, distribution = "Temurin")Once you have Java JDK set up correctly, the current supported version of Tetrad can then be installed by calling
To verify everything is set up correctly you can run
verify_tetrad():
verify_tetrad()
#> $installed
#> [1] TRUE
#>
#> $version
#> [1] "7.6.10"
#>
#> $java_ok
#> [1] TRUE
#>
#> $java_version
#> [1] "25.0.2"
#>
#> $message
#> [1] "Tetrad version 7.6.10 is installed and ready to use."Now you should be able to use Tetrad as an engine for the GES algorithm as shown in the code chunk below.
if (verify_tetrad()$installed && verify_tetrad()$java_ok) {
ges_tetrad <- ges(
engine = "tetrad", # Use the Tetrad implementation
score = "ebic" # Use the EBIC score
)
ges_result_tetrad <- disco(data = num_data, method = ges_tetrad)
plot(ges_result_tetrad, layout = plot_layout, main = "GES EBIC (Tetrad)")
}
If you want to customize the plot appearance further, you can pass
additional arguments to plot(). For example, to change the
appearance of the nodes, you can use the node_style
argument:
plot(
pc_result_bnlearn,
layout = plot_layout,
main = "Customized plot",
node_style = list(
fill = "lightblue", # Fill color
col = "darkblue", # Border color
lwd = 2, # Border width
padding = 4, # Text padding (mm)
size = 1.2 # Size multiplier
)
)
For more details on customizing plots and generating TikZ code for LaTeX documents, see the visualization vignette.
Instead of using plot(), another way to view and analyze
the results is to use the print() or summary()
functions:
print(pc_result_bnlearn)
#>
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#>
#> ── Edges ──
#>
#> from edge to
#> <chr> <chr> <chr>
#> 1 X1 --> Y
#> 2 X1 --- Z
#> 3 X2 --- X3
#> 4 X2 --> Y
#> 5 X3 --> Y
#> 6 Z --> Y
#> ── Nodes ──
#> name
#> <chr>
#> 1 X1
#> 2 X2
#> 3 X3
#> 4 Z
#> 5 Y
#> ── Knowledge object ────────────────────────────────────────────────────────────
summary(pc_result_bnlearn)
#>
#> ── caugi graph summary ─────────────────────────────────────────────────────────
#> Graph class: PDAG
#> Nodes: 5
#> Edges: 6
#>
#> ── Knowledge summary ──
#>
#> Tiers: 0
#> Variables: 0
#> Required edges: 0
#> Forbidden edges: 0
#>
#> ── Variables per Tier
