GES Algorithm for Causal Discovery

Run the Greedy Equivalent Search algorithm for causal discovery using one of several engines.

Usage

ges(engine = c("tetrad", "pcalg"), score, ...)

Arguments

engine

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

"tetrad"

Tetrad Java library.

"pcalg"

pcalg R package.

score

Character; name of the scoring function to use.

...

Additional arguments passed to the chosen engine (e.g. score and algorithm parameters).

Details

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

• TetradSearch for Tetrad (note, Tetrad refers to it as "fges"),

• PcalgSearch for pcalg.

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

Chickering, D. M. (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research 3, 507-554.

See also

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

Examples

data(tpc_example)

#### Using pcalg engine ####
# Recommended path using disco()
ges_pcalg <- ges(engine = "pcalg", score = "sem_bic")
disco(tpc_example, ges_pcalg)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x2  -->   oldage_x5
#> 3 child_x2  ---   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  -->   oldage_x5
#> 6 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 ────────────────────────────────────────────────────────────

# or using ges_pcalg directly
ges_pcalg(tpc_example)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x2  -->   oldage_x5
#> 3 child_x2  ---   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  -->   oldage_x5
#> 6 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
ges_pcalg <- ges(
  engine = "pcalg",
  score = "sem_bic",
  adaptive = "vstructures",
  phase = "forward",
  iterate = FALSE,
  maxDegree = 3,
  verbose = FALSE
)
disco(tpc_example, ges_pcalg)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x2  -->   oldage_x5
#> 3 child_x2  ---   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  -->   oldage_x5
#> 6 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 ────────────────────────────────────────────────────────────


#### Using tetrad engine with tier knowledge ####
# Requires Tetrad to be installed
if (verify_tetrad()$installed && verify_tetrad()$java_ok) {
  kn <- knowledge(
    tpc_example,
    tier(
      child ~ tidyselect::starts_with("child"),
      youth ~ tidyselect::starts_with("youth"),
      oldage ~ tidyselect::starts_with("oldage")
    )
  )

  # Recommended path using disco()
  ges_tetrad <- ges(engine = "tetrad", score = "sem_bic")
  disco(tpc_example, ges_tetrad, knowledge = kn)

  # or using ges_tetrad directly
  ges_tetrad <- ges_tetrad |> set_knowledge(kn)
  ges_tetrad(tpc_example)
}
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: UNKNOWN
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x2  ---   child_x1 
#> 2 child_x2  -->   oldage_x5
#> 3 child_x2  -->   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  -->   oldage_x5
#> 6 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
if (verify_tetrad()$installed && verify_tetrad()$java_ok) {
  ges_tetrad <- ges(
    engine = "tetrad",
    score = "ebic",
    symmetric_first_step = TRUE,
    max_degree = 3,
    parallelized = TRUE,
    faithfulness_assumed = TRUE
  )
  disco(tpc_example, ges_tetrad)
}
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x2  ---   child_x1 
#> 2 child_x2  -->   oldage_x5
#> 3 child_x2  ---   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  -->   oldage_x5
#> 6 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 ────────────────────────────────────────────────────────────