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GS Algorithm for Causal Discovery

Source: R/gs.R
gs.Rd

Run the 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:

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.

References

Margaritis, D., Thrun, S.: Bayesian network induction via local neighborhoods. Tech. rep., DTIC Document (2000).

See also

Other causal discovery algorithms: boss(), boss_fci(), fci(), ges(), gfci(), grasp(), grasp_fci(), iamb-family, pc(), rfci(), sp_fci(), tfci(), tges(), tpc()

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)
#> <Disco MPDAG: 6 nodes | 9 edges | Knowledge: 4 required>
#> Learned graph:
#>   nodes: child_x2, child_x1, youth_x4, youth_x3, oldage_x6, oldage_x5
#>   edges: child_x1---child_x2, child_x1-->youth_x3, child_x1-->youth_x4
#>          child_x2-->oldage_x5, child_x2-->youth_x3, child_x2-->youth_x4
#>          oldage_x5-->oldage_x6, youth_x3-->oldage_x5, youth_x4-->oldage_x6
#> Knowledge:
#>   vars: child_x1, child_x2, oldage_x5, oldage_x6, youth_x3, youth_x4
#>   required:
#>     child_x1-->youth_x3 + youth_x4
#>     child_x2-->youth_x3 + youth_x4

# or using gs_bnlearn directly
gs_bnlearn <- gs_bnlearn |> set_knowledge(kn)
gs_bnlearn(tpc_example)
#> <Disco PDAG: 6 nodes | 9 edges>
#>   nodes: child_x2, child_x1, youth_x4, youth_x3, oldage_x6, oldage_x5
#>   edges: child_x1---child_x2, child_x1-->youth_x3, child_x1-->youth_x4
#>          child_x2-->oldage_x5, child_x2-->youth_x3, child_x2-->youth_x4
#>          oldage_x5-->oldage_x6, youth_x3-->oldage_x5, youth_x4-->oldage_x6


# 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)
#> <Disco CPDAG: 6 nodes | 5 edges>
#>   nodes: child_x2, child_x1, youth_x4, youth_x3, oldage_x6, oldage_x5
#>   edges: child_x1---child_x2, child_x2---oldage_x6, child_x2---youth_x4
#>          oldage_x6---youth_x3, oldage_x6---youth_x4