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IAMB Family of Causal Discovery Algorithms

Source: R/aaa-iamb-family.R, R/iamb-family.R
iamb-family.Rd

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, ...)

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).

Details

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

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(), 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)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x1  -->   youth_x3 
#> 3 child_x1  -->   youth_x4 
#> 4 child_x2  -->   oldage_x5
#> 5 child_x2  -->   youth_x3 
#> 6 child_x2  -->   youth_x4 
#> 7 oldage_x5 -->   oldage_x6
#> 8 youth_x3  -->   oldage_x5
#> 9 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 ────────────────────────────────────────────────────────────
#> 
#> ── Variables ──
#> 
#>   var       tier 
#>   <chr>     <chr>
#> 1 child_x1  NA   
#> 2 child_x2  NA   
#> 3 oldage_x5 NA   
#> 4 oldage_x6 NA   
#> 5 youth_x3  NA   
#> 6 youth_x4  NA   
#> ── Edges ──
#> 
#>  ✔  child_x1 → youth_x3
#>  ✔  child_x1 → youth_x4
#>  ✔  child_x2 → youth_x3
#>  ✔  child_x2 → youth_x4

# or using iamb_bnlearn directly
iamb_bnlearn <- iamb_bnlearn |> set_knowledge(kn)
iamb_bnlearn(tpc_example)
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x1  -->   youth_x3 
#> 3 child_x1  -->   youth_x4 
#> 4 child_x2  -->   oldage_x5
#> 5 child_x2  -->   youth_x3 
#> 6 child_x2  -->   youth_x4 
#> 7 oldage_x5 -->   oldage_x6
#> 8 youth_x3  -->   oldage_x5
#> 9 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
iamb_bnlearn <- iamb(
  engine = "bnlearn",
  test = "fisher_z",
  alpha = 0.05,
  max.sx = 2,
  debug = FALSE,
  undirected = TRUE
)

disco(tpc_example, iamb_bnlearn)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x2  ---   oldage_x5
#> 3 oldage_x5 ---   oldage_x6
#> 4 oldage_x5 ---   youth_x3 
#> 5 oldage_x6 ---   youth_x4 
#> ── Nodes ──
#> 
#>   name     
#>   <chr>    
#> 1 child_x2 
#> 2 child_x1 
#> 3 youth_x4 
#> 4 youth_x3 
#> 5 oldage_x6
#> 6 oldage_x5
#> ── Knowledge object ────────────────────────────────────────────────────────────

##### iamb_fdr #####

iamb_fdr_bnlearn <- iamb_fdr(
  engine = "bnlearn",
  test = "fisher_z",
  alpha = 0.05
)
disco(tpc_example, iamb_fdr_bnlearn, knowledge = kn)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x1  -->   youth_x3 
#> 3 child_x1  -->   youth_x4 
#> 4 child_x2  -->   oldage_x5
#> 5 child_x2  -->   youth_x3 
#> 6 child_x2  -->   youth_x4 
#> 7 oldage_x5 -->   oldage_x6
#> 8 youth_x3  -->   oldage_x5
#> 9 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 ────────────────────────────────────────────────────────────
#> 
#> ── Variables ──
#> 
#>   var       tier 
#>   <chr>     <chr>
#> 1 child_x1  NA   
#> 2 child_x2  NA   
#> 3 oldage_x5 NA   
#> 4 oldage_x6 NA   
#> 5 youth_x3  NA   
#> 6 youth_x4  NA   
#> ── Edges ──
#> 
#>  ✔  child_x1 → youth_x3
#>  ✔  child_x1 → youth_x4
#>  ✔  child_x2 → youth_x3
#>  ✔  child_x2 → youth_x4

##### fast_iamb #####

fast_iamb_bnlearn <- fast_iamb(
  engine = "bnlearn",
  test = "fisher_z",
  alpha = 0.05
)
disco(tpc_example, fast_iamb_bnlearn, knowledge = kn)
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x1  -->   youth_x3 
#> 3 child_x1  -->   youth_x4 
#> 4 child_x2  -->   oldage_x5
#> 5 child_x2  -->   youth_x3 
#> 6 child_x2  -->   youth_x4 
#> 7 oldage_x5 -->   oldage_x6
#> 8 youth_x3  -->   oldage_x5
#> 9 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 ────────────────────────────────────────────────────────────
#> 
#> ── Variables ──
#> 
#>   var       tier 
#>   <chr>     <chr>
#> 1 child_x1  NA   
#> 2 child_x2  NA   
#> 3 oldage_x5 NA   
#> 4 oldage_x6 NA   
#> 5 youth_x3  NA   
#> 6 youth_x4  NA   
#> ── Edges ──
#> 
#>  ✔  child_x1 → youth_x3
#>  ✔  child_x1 → youth_x4
#>  ✔  child_x2 → youth_x3
#>  ✔  child_x2 → youth_x4

#### inter_iamb #####

inter_iamb_bnlearn <- inter_iamb(
  engine = "bnlearn",
  test = "fisher_z",
  alpha = 0.05
)
disco(tpc_example, inter_iamb_bnlearn, knowledge = kn)
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x1  -->   youth_x3 
#> 3 child_x1  -->   youth_x4 
#> 4 child_x2  -->   oldage_x5
#> 5 child_x2  -->   youth_x3 
#> 6 child_x2  -->   youth_x4 
#> 7 oldage_x5 -->   oldage_x6
#> 8 youth_x3  -->   oldage_x5
#> 9 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 ────────────────────────────────────────────────────────────
#> 
#> ── Variables ──
#> 
#>   var       tier 
#>   <chr>     <chr>
#> 1 child_x1  NA   
#> 2 child_x2  NA   
#> 3 oldage_x5 NA   
#> 4 oldage_x6 NA   
#> 5 youth_x3  NA   
#> 6 youth_x4  NA   
#> ── Edges ──
#> 
#>  ✔  child_x1 → youth_x3
#>  ✔  child_x1 → youth_x4
#>  ✔  child_x2 → youth_x3
#>  ✔  child_x2 → youth_x4