pc: PC Algorithm for Causal Discovery

In causalDisco: Tools for Causal Discovery on Observational Data

PC Algorithm for Causal Discovery

Description

Run the Peter-Clark algorithm for causal discovery using one of several engines.

Usage

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

Arguments

engine	Character; which engine to use. Must be one of: "tetrad" Tetrad Java library. "pcalg" pcalg R package. "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:

• TetradSearch for Tetrad,

• PcalgSearch for pcalg,

• 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.

References

Spirtes P, Glymour C, and Scheines R. Causation, Prediction, and Search. MIT Press, 2000.

See Also

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

Examples

data(tpc_example)

#### Using pcalg engine ####
# Recommended path using disco()
pc_pcalg <- pc(engine = "pcalg", test = "fisher_z", alpha = 0.05)
disco(tpc_example, pc_pcalg)

# or using pc_pcalg directly
pc_pcalg(tpc_example)

# With all algorithm arguments specified
pc_pcalg <- pc(
  engine = "pcalg",
  test = "fisher_z",
  alpha = 0.05,
  fixedGaps = NULL,
  fixedEdges = NULL,
  NAdelete = FALSE,
  m.max = 10,
  u2pd = "relaxed",
  skel.method = "original",
  conservative = TRUE,
  maj.rule = FALSE,
  solve.confl = TRUE,
  numCores = 1,
  verbose = FALSE
)

#### Using bnlearn engine with required knowledge ####
kn <- knowledge(
  tpc_example,
  starts_with("child") %-->% starts_with("youth")
)


# Recommended path using disco()
pc_bnlearn <- pc(engine = "bnlearn", test = "fisher_z", alpha = 0.05)
disco(tpc_example, pc_bnlearn, knowledge = kn)

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


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

disco(tpc_example, pc_bnlearn)

#### 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()
  pc_tetrad <- pc(engine = "tetrad", test = "fisher_z", alpha = 0.05)
  disco(tpc_example, pc_tetrad, knowledge = kn)

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

# With all algorithm arguments specified
if (verify_tetrad()$installed && verify_tetrad()$java_ok) {
  pc_tetrad <- pc(
    engine = "tetrad",
    test = "fisher_z",
    alpha = 0.05,
    conflict_rule = 2,
    depth = 10,
    stable_fas = FALSE,
    guarantee_cpdag = TRUE
  )
  disco(tpc_example, pc_tetrad)
}

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