This vignette illustrates how to add a new algorithm to causalDisco from bnlearn, pcalg and Tetrad. Both bnlearn and pcalg are R packages and therefore share the same interface, while Tetrad is a Java library and requires a slightly different integration pattern.

bnlearn and pcalg

Suppose we want to add the Hybrid Parents and Children (HPC) algorithm from bnlearn. We can integrate it into causalDisco by creating a new function hpc().

HPC is a constraint-based algorithm, so it requires a conditional independence test and a significance level alpha. It returns a PDAG, so we set the graph_class attribute to "PDAG".

The function takes an engine argument, which specifies which implementation of the algorithm to use. Even though we only add HPC from bnlearn in this example, we set up the structure to allow for multiple engines in the future. The ... argument allows us to pass additional arguments to the underlying algorithm and test.

The code for the hpc() function is as follows:

hpc <- function(
  engine = c("bnlearn"),
  test,
  alpha = 0.05,
  ...
) {
  engine <- match.arg(engine)
  args <- rlang::list2(...)

  builder <- function(knowledge = NULL) {
    runner <- switch(
      engine,
      bnlearn = rlang::exec(
        hpc_bnlearn_runner,
        test = test,
        alpha = alpha,
        !!!args
      )
    )
    runner
  }
  method <- new_disco_method(
    builder = builder,
    name = "hpc",
    engine = engine,
    graph_class = "PDAG"
  )
  method
}

We see that the hpc() function needs two components:

Builder function: Constructs a runner for the algorithm. It configures the algorithm with the provided parameters and optional background knowledge.
Method object: Created via new_disco_method(). It encapsulates the builder and metadata about the method. This allows us to be able to call disco() to execute the algorithm on a dataset.

Next, we define the runner. The runner handles configuring the search object with the chosen test, alpha level, and any additional arguments, and it actually runs the search on the data when called. For bnlearn algorithms, we use the BnlearnSearch R6 class to manage search configuration and execution. We also use distribute_engine_args() to determine which extra arguments should be passed to the test, the algorithm, or both.

hpc_bnlearn_runner <- function(test, alpha, ...) {
  args <- list(...)
  search <- BnlearnSearch$new()
  args_to_pass <- distribute_engine_args(
    search = search,
    args = args,
    engine = "bnlearn",
    alg = "hpc"
  )

  search$set_test(test, alpha)
  search$set_alg("hpc", args_to_pass)

  runner <- list(
    set_knowledge = function(knowledge) {
      search$set_knowledge(knowledge)
    },
    run = function(data) {
      search$run_search(data)
    }
  )
  runner
}

Once defined, the HPC algorithm can be used like any other method in causalDisco. We first construct the method using hpc(), and then pass it to disco(). Here we demonstrate using the included tpc_example dataset:

data(tpc_example)
hpc_bnlearn <- hpc(engine = "bnlearn", test = "fisher_z", alpha = 0.05)
hpc_bnlearn_result <- disco(tpc_example, hpc_bnlearn)
plot(hpc_bnlearn_result)

To implement a score-based algorithm instead, the structure would remain the same. The main difference is that the method would accept a score argument rather than test and alpha, and the runner would call search$set_score() instead of search$set_test(). A hybrid algorithm algorithm would accept test, alpha, and score arguments, and the runner would call both set_test() and set_score().

To implement an algorithm from pcalg rather than bnlearn, we would follow the same structure but use the R6 class PcalgSearch instead of BnlearnSearch in the runner (i.e., PcalgSearch$new()).

Tetrad

We will illustrate how to make a custom version (which actually does the same as our implementation, just with some defensive programming omitted) of the Boss algorithm from Tetrad. Tetrad algorithms follow a slightly different integration pattern because Tetrad is a Java library rather than an R package.

To add a new Tetrad algorithm, first register it with register_tetrad_algorithm(). This function requires:

The algorithm name.
A setup function that configures a TetradSearch object to execute the algorithm.

The setup function receives the TetradSearch object as its first argument, along with any additional parameters passed via ... when the method is constructed. Its responsibilities are to:

Configure the TetradSearch object with the appropriate parameters and score.
Instantiate the correct Tetrad algorithm class.

The fully qualified class name can be identified by inspecting the Tetrad source code at:

https://github.com/cmu-phil/tetrad

Be sure to browse the version corresponding to the installed Tetrad release. The relevant Java classes are located under:

https://github.com/cmu-phil/tetrad/tree/development/tetrad-lib/src/main/java

register_tetrad_algorithm(
  "my_boss_variant",
  function(
    self,
    num_starts = 1,
    use_bes = TRUE,
    use_data_order = TRUE,
    output_cpdag = TRUE
  ) {
    self$set_params(
      USE_BES = use_bes,
      NUM_STARTS = num_starts,
      USE_DATA_ORDER = use_data_order,
      OUTPUT_CPDAG = output_cpdag
    )

    self$alg <- rJava::.jnew(
      "edu/cmu/tetrad/algcomparison/algorithm/oracle/cpdag/Boss",
      self$score
    )
    self$alg$setKnowledge(self$knowledge)
  }
)

You can view all the custom registered Tetrad algorithms using tetrad_alg_registry(), and reset it using reset_tetrad_alg_registry().

Like for bnlearn and pcalg, we then create a method function my_boss_variant() that constructs a builder function which in turn constructs a runner using the registered Tetrad algorithm.

BOSS is a score-based algorithm, so the method accepts a score argument and passes it to the runner, which configures the TetradSearch object accordingly. It returns a PDAG, so we set the graph_class attribute to "PDAG".

my_boss_variant <- function(
  engine = "tetrad",
  score,
  ...
) {
  engine <- match.arg(engine)
  args <- rlang::list2(...)

  builder <- function(knowledge = NULL) {
    runner <- switch(
      engine,
      tetrad = rlang::exec(my_boss_variant_tetrad_runner, score, !!!args)
    )
    runner
  }

  method <- new_disco_method(
    builder = builder,
    name = "my_boss_variant",
    engine = engine,
    graph_class = "PDAG"
  )
  method
}

Next we define the runner function my_boss_variant_tetrad_runner()

my_boss_variant_tetrad_runner <- function(score, ...) {
  search <- TetradSearch$new()
  args <- list(...)
  args_to_pass <- distribute_engine_args(
    search = search,
    args = args,
    engine = "tetrad",
    alg = "my_boss_variant"
  )

  if (length(args_to_pass$score_args) > 0) {
    rlang::exec(search$set_score, score, !!!args_to_pass$score_args)
  } else {
    search$set_score(score)
  }

  if (length(args_to_pass$alg_args) > 0) {
    rlang::exec(search$set_alg, "my_boss_variant", !!!args_to_pass$alg_args)
  } else {
    search$set_alg("my_boss_variant")
  }

  runner <- list(
    set_knowledge = function(knowledge) {
      search$set_knowledge(knowledge)
    },
    run = function(data) {
      search$run_search(data)
    }
  )
  runner
}

Once defined we can now run my_boss_variant() like any other method in causalDisco.

# Ensure Tetrad is installed and Java is working before running the algorithm
if (verify_tetrad()$installed && verify_tetrad()$java_ok) {
  my_boss_variant_tetrad <- my_boss_variant(engine = "tetrad", score = "sem_bic")
  my_boss_variant_tetrad_result <- disco(tpc_example, my_boss_variant_tetrad)
  plot(my_boss_variant_tetrad_result)
}

Once again, if using a hybrid or constraint-based algorithm rather than a score-based one, the structure would remain the same but the method would accept different arguments (e.g., test and alpha for a constraint-based algorithm).

Finally, we clean up the custom registered Tetrad algorithm

reset_tetrad_alg_registry()

Extending causalDisco with new algorithms

bnlearn and pcalg

Tetrad

Conclusion