GS Algorithm for Causal Discovery

Run the GS (Grow-Shrink) algorithm for causal discovery using one of several engines.

Usage

gs(engine = c("bnlearn"), test, alpha = 0.05, ...)

Arguments

engine

Character; which engine to use. Must be one of:

"bnlearn": bnlearn R package.

test

Character; name of the conditional‐independence test.

alpha

Numeric; significance level for the CI tests.

...

Additional arguments passed to the chosen engine (e.g. test or algorithm parameters).

Details

For specific details on the supported tests and parameters for each engine, see:

BnlearnSearch for bnlearn.

Recommendation

While it is possible to call the function returned directly with a data frame, we recommend using disco(). This provides a consistent interface and handles knowledge integration.

Value

A function that takes a single argument data (a data frame). When called, this function returns a list containing:

knowledge A Knowledge object with the background knowledge used in the causal discovery algorithm. See knowledge() for how to construct it.
caugi A caugi::caugi object (of class PDAG) representing the learned causal graph from the causal discovery algorithm.

Examples

data(tpc_example)

kn <- knowledge(
  tpc_example,
  starts_with("child") %-->% starts_with("youth")
)


# Recommended path using disco()
gs_bnlearn <- gs(
  engine = "bnlearn",
  test = "fisher_z",
  alpha = 0.05
)
disco(tpc_example, gs_bnlearn, knowledge = kn)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x1  -->   youth_x3 
#> 3 child_x1  -->   youth_x4 
#> 4 child_x2  -->   oldage_x5
#> 5 child_x2  -->   youth_x3 
#> 6 child_x2  -->   youth_x4 
#> 7 oldage_x5 -->   oldage_x6
#> 8 youth_x3  -->   oldage_x5
#> 9 youth_x4  -->   oldage_x6
#> ── Nodes ──
#> 
#>   name     
#>   <chr>    
#> 1 child_x2 
#> 2 child_x1 
#> 3 youth_x4 
#> 4 youth_x3 
#> 5 oldage_x6
#> 6 oldage_x5
#> ── Knowledge object ────────────────────────────────────────────────────────────
#> 
#> ── Variables ──
#> 
#>   var       tier 
#>   <chr>     <chr>
#> 1 child_x1  NA   
#> 2 child_x2  NA   
#> 3 oldage_x5 NA   
#> 4 oldage_x6 NA   
#> 5 youth_x3  NA   
#> 6 youth_x4  NA   
#> ── Edges ──
#> 
#>  ✔  child_x1 → youth_x3
#>  ✔  child_x1 → youth_x4
#>  ✔  child_x2 → youth_x3
#>  ✔  child_x2 → youth_x4

# or using gs_bnlearn directly
gs_bnlearn <- gs_bnlearn |> set_knowledge(kn)
gs_bnlearn(tpc_example)
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x1  -->   youth_x3 
#> 3 child_x1  -->   youth_x4 
#> 4 child_x2  -->   oldage_x5
#> 5 child_x2  -->   youth_x3 
#> 6 child_x2  -->   youth_x4 
#> 7 oldage_x5 -->   oldage_x6
#> 8 youth_x3  -->   oldage_x5
#> 9 youth_x4  -->   oldage_x6
#> ── Nodes ──
#> 
#>   name     
#>   <chr>    
#> 1 child_x2 
#> 2 child_x1 
#> 3 youth_x4 
#> 4 youth_x3 
#> 5 oldage_x6
#> 6 oldage_x5
#> ── Knowledge object ────────────────────────────────────────────────────────────


# With all algorithm arguments specified
gs_bnlearn <- gs(
  engine = "bnlearn",
  test = "fisher_z",
  alpha = 0.05,
  max.sx = 2,
  debug = FALSE,
  undirected = TRUE
)

disco(tpc_example, gs_bnlearn)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x2  ---   oldage_x6
#> 3 child_x2  ---   youth_x4 
#> 4 oldage_x6 ---   youth_x3 
#> 5 oldage_x6 ---   youth_x4 
#> ── Nodes ──
#> 
#>   name     
#>   <chr>    
#> 1 child_x2 
#> 2 child_x1 
#> 3 youth_x4 
#> 4 youth_x3 
#> 5 oldage_x6
#> 6 oldage_x5
#> ── Knowledge object ────────────────────────────────────────────────────────────