FCI Algorithm for Causal Discovery

Run the FCI algorithm for causal discovery using one of several engines.

Usage

fci(engine = c("tetrad", "pcalg"), test, alpha = 0.05, ...)

Arguments

engine

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

"tetrad"

Tetrad Java library.

"pcalg"

pcalg 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:

• TetradSearch for Tetrad,

• 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 representing the learned causal graph. This graph is a PAG (Partial Ancestral Graph), but since PAGs are not yet natively supported in caugi, it is currently stored with class UNKNOWN.

See also

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

Examples

data(tpc_example)

# Recommended path using disco()
fci_pcalg <- fci(engine = "pcalg", test = "fisher_z", alpha = 0.05)
disco(tpc_example, fci_pcalg)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: UNKNOWN
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x2  o-o   child_x1 
#> 2 child_x2  o->   oldage_x5
#> 3 child_x2  o-o   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  o->   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 fci_pcalg directly
fci_pcalg(tpc_example)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: UNKNOWN
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x2  o-o   child_x1 
#> 2 child_x2  o->   oldage_x5
#> 3 child_x2  o-o   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  o->   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
fci_pcalg <- fci(
  engine = "pcalg",
  test = "fisher_z",
  alpha = 0.05,
  skel.method = "original",
  type = "anytime",
  fixedGaps = NULL,
  fixedEdges = NULL,
  NAdelete = FALSE,
  m.max = 10,
  pdsep.max = 2,
  rules = c(rep(TRUE, 9), FALSE),
  doPdsep = FALSE,
  biCC = TRUE,
  conservative = TRUE,
  maj.rule = FALSE,
  numCores = 1,
  selectionBias = FALSE,
  jci = "1",
  verbose = FALSE
)
disco(tpc_example, fci_pcalg)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: UNKNOWN
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x2  o-o   child_x1 
#> 2 child_x2  o->   oldage_x5
#> 3 child_x2  o-o   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  o->   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()
  fci_tetrad <- fci(engine = "tetrad", test = "fisher_z", alpha = 0.05)
  disco(tpc_example, fci_tetrad, knowledge = kn)

  # or using fci_tetrad directly
  fci_tetrad <- fci_tetrad |> set_knowledge(kn)
  fci_tetrad(tpc_example)
}
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: UNKNOWN
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x2  o-o   child_x1 
#> 2 child_x2  o->   oldage_x5
#> 3 child_x2  o-o   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  o->   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) {
  fci_tetrad <- fci(
    engine = "tetrad",
    test = "fisher_z",
    alpha = 0.05,
    complete_rule_set_used = FALSE,
    max_disc_path_length = 4,
    depth = 10,
    stable_fas = FALSE,
    guarantee_pag = TRUE
  )
  disco(tpc_example, fci_tetrad)
}
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: UNKNOWN
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  o->   child_x2 
#> 2 child_x2  <->   oldage_x5
#> 3 child_x2  <->   youth_x4 
#> 4 oldage_x6 <->   oldage_x5
#> 5 youth_x3  o->   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 ────────────────────────────────────────────────────────────