Run the TGES Algorithm for Causal Discovery

Perform causal discovery using the temporal greedy equivalence search algorithm.

Usage

tges_run(score, verbose = FALSE)

Arguments

score

tiered scoring object to be used. At the moment only scores supported are

• TemporalBIC and

• TemporalBDeu.

verbose

indicates whether debug output should be printed.

Value

A Disco object (a list with a caugi and a Knowledge object).

Author

Tobias Ellegaard Larsen

Examples

# Recommended route using disco:
kn <- knowledge(
  tpc_example,
  tier(
    child ~ starts_with("child"),
    youth ~ starts_with("youth"),
    old ~ starts_with("old")
  )
)

my_tges <- tges(engine = "causalDisco", score = "tbic")

disco(tpc_example, my_tges, knowledge = kn)
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x2  -->   oldage_x5
#> 3 child_x2  -->   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  -->   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 ────────────────────────────────────────────────────────────
#> 
#> ── Tiers ──
#> 
#>   tier 
#>   <chr>
#> 1 child
#> 2 youth
#> 3 old  
#> ── Variables ──
#> 
#>   var       tier 
#>   <chr>     <chr>
#> 1 child_x1  child
#> 2 child_x2  child
#> 3 youth_x3  youth
#> 4 youth_x4  youth
#> 5 oldage_x5 old  
#> 6 oldage_x6 old  

# another way to run it

my_tges <- my_tges |>
  set_knowledge(kn)
my_tges(tpc_example)
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x2  -->   oldage_x5
#> 3 child_x2  -->   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  -->   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 you can run directly with tges_run()

data(tpc_example)

score_bic <- new(
  "TemporalBIC",
  data = tpc_example,
  nodes = colnames(tpc_example),
  knowledge = kn
)

res_bic <- tges_run(score_bic)
res_bic
#> 
#> ── caugi graph ─────────────────────────────────────────────────────────────────
#> Graph class: PDAG
#> 
#> ── Edges ──
#> 
#>   from      edge  to       
#>   <chr>     <chr> <chr>    
#> 1 child_x1  ---   child_x2 
#> 2 child_x2  -->   oldage_x5
#> 3 child_x2  -->   youth_x4 
#> 4 oldage_x5 -->   oldage_x6
#> 5 youth_x3  -->   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 ────────────────────────────────────────────────────────────