causalDisco (version 1.0.1)

iamb-family: IAMB Family of Causal Discovery Algorithms

Description

Functions for causal discovery using variants of the Incremental Association algorithm:

• iamb: Incremental Association (IAMB)

• inter_iamb: Interleaved Incremental Association (Inter-IAMB)

• iamb_fdr: Incremental Association with FDR (IAMB-FDR)

• fast_iamb: Fast Incremental Association (Fast-IAMB)

Usage

iamb(engine = c("bnlearn"), test, alpha = 0.05, ...)
iamb_fdr(engine = c("bnlearn"), test, alpha = 0.05, ...)
fast_iamb(engine = c("bnlearn"), test, alpha = 0.05, ...)
inter_iamb(engine = c("bnlearn"), test, alpha = 0.05, ...)

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.

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).

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.

Details

Each function supports the same engines and parameters. For details on tests and parameters for each engine, see:

• BnlearnSearch for bnlearn.

See Also

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

Examples

data(tpc_example)

kn <- knowledge(
  tpc_example,
  starts_with("child") %-->% starts_with("youth")
)

##### iamb #####

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

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


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

disco(tpc_example, iamb_bnlearn)

##### iamb_fdr #####

iamb_fdr_bnlearn <- iamb_fdr(
  engine = "bnlearn",
  test = "fisher_z",
  alpha = 0.05
)
disco(tpc_example, iamb_fdr_bnlearn, knowledge = kn)

##### fast_iamb #####

fast_iamb_bnlearn <- fast_iamb(
  engine = "bnlearn",
  test = "fisher_z",
  alpha = 0.05
)
disco(tpc_example, fast_iamb_bnlearn, knowledge = kn)

#### inter_iamb #####

inter_iamb_bnlearn <- inter_iamb(
  engine = "bnlearn",
  test = "fisher_z",
  alpha = 0.05
)
disco(tpc_example, inter_iamb_bnlearn, knowledge = kn)