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Run the TGES Algorithm for Causal Discovery

Source: R/tges-run.R
tges_run.Rd

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

verbose

indicates whether debug output should be printed.

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.

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)
#> <Disco MPDAG: 6 nodes | 6 edges | Knowledge: 3 tiers>
#> Learned graph:
#>   nodes: child_x2, child_x1, youth_x4, youth_x3, oldage_x6, oldage_x5
#>   edges: child_x1---child_x2, child_x2-->oldage_x5, child_x2-->youth_x4
#>          oldage_x5-->oldage_x6, youth_x3-->oldage_x5, youth_x4-->oldage_x6
#> Knowledge:
#>   tier(child): child_x1, child_x2
#>   tier(youth): youth_x3, youth_x4
#>   tier(old): oldage_x5, oldage_x6

# another way to run it

my_tges <- my_tges |>
  set_knowledge(kn)
my_tges(tpc_example)
#> <Disco PDAG: 6 nodes | 6 edges>
#>   nodes: child_x2, child_x1, youth_x4, youth_x3, oldage_x6, oldage_x5
#>   edges: child_x1---child_x2, child_x2-->oldage_x5, child_x2-->youth_x4
#>          oldage_x5-->oldage_x6, youth_x3-->oldage_x5, youth_x4-->oldage_x6


# 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
#> <Disco PDAG: 6 nodes | 6 edges>
#>   nodes: child_x2, child_x1, youth_x4, youth_x3, oldage_x6, oldage_x5
#>   edges: child_x1---child_x2, child_x2-->oldage_x5, child_x2-->youth_x4
#>          oldage_x5-->oldage_x6, youth_x3-->oldage_x5, youth_x4-->oldage_x6