TGES Algorithm for Causal Discovery

Run the Temporal GES algorithm for causal discovery using the causalDisco engine.

Usage

tges(engine = c("causalDisco"), score, ...)

Arguments

engine

Character; which engine to use. Must be one of:

"causalDisco"

causalDisco library.

score

Character; name of the scoring function to use.

...

Additional arguments passed to the chosen engine (e.g. test or algorithm parameters).

Details

For specific details on the supported scores, see CausalDiscoSearch. For additional parameters passed via ..., see tges_run().

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.

See also

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

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 ────────────────────────────────────────────────────────────