gfci: GFCI Algorithm for Causal Discovery

In causalDisco: Tools for Causal Discovery on Observational Data

GFCI Algorithm for Causal Discovery

Description

Run the Greedy Fast Causal Inference algorithm for causal discovery using one of several engines.

Usage

gfci(engine = "tetrad", score, test, alpha = 0.05, ...)

Arguments

engine	Character; which engine to use. Must be one of: "tetrad" Tetrad Java library.
score	Character; name of the scoring function to use.
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. score and algorithm parameters).

Details

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

• TetradSearch for Tetrad.

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.

References

Ogarrio, J. M., Spirtes, P., and Ramsey, J. (2016). A hybrid causal search algorithm for latent variable models. In Conference on probabilistic graphical models, pages 368–379. PMLR.

See Also

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

Examples

data(num_data)

# Requires Tetrad to be installed
if (verify_tetrad()$installed && verify_tetrad()$java_ok) {
  # Recommended path using disco()
  gfci_tetrad <- gfci(
    engine = "tetrad",
    score = "sem_bic",
    test = "fisher_z"
  )
  disco(tpc_example, gfci_tetrad)

  # or using gfci_tetrad directly
  gfci_tetrad(tpc_example)
}

#### With tier knowledge ####
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()
  gfci_tetrad <- gfci(
    engine = "tetrad",
    score = "sem_bic",
    test = "fisher_z"
  )
  disco(tpc_example, gfci_tetrad, knowledge = kn)

  # or using gfci_tetrad directly
  gfci_tetrad <- gfci_tetrad |> set_knowledge(kn)
  gfci_tetrad(tpc_example)
}

# With all algorithm arguments specified
if (verify_tetrad()$installed && verify_tetrad()$java_ok) {
  gfci_tetrad <- gfci(
    engine = "tetrad",
    score = "poisson_prior",
    test = "rank_independence",
    depth = 3,
    max_degree = 2,
    max_disc_path_length = 5,
    use_heuristic = FALSE,
    complete_rule_set_used = FALSE,
    guarantee_pag = TRUE,
    start_complete = TRUE,
    num_threads = 2,
    verbose = TRUE
  )
  disco(num_data, gfci_tetrad)
}

causalDisco documentation built on April 13, 2026, 5:06 p.m.