TemporalBDeu-class: Temporal Bayesian Dirichlet equivalent uniform (Score...

In causalDisco: Tools for Causal Discovery on Observational Data

Temporal Bayesian Dirichlet equivalent uniform (Score criterion)

Description

A reference class for categorical observational data Scoring with Tiered Background Knowledge. This class represents a score for causal discovery using tiered background knowledge from observational categorical data; it is used in the causal discovery function tges.

Arguments

data	A numeric matrix with n rows and p columns. Each row corresponds to one observational realization.
order	A vector specifying the order each variable. Can be either a vector of integers or an vector of prefixes. If integers, such that the ith entry will detail the order of the ith variable in the dataset. Must start at 1 an increase with increments of 1. If prefixes, must be in order.
iss	Imaginary Sample Size (ISS), also referred to as Equivalent Sample Size (ESS), determines how much weight is assigned to the prior in terms of the size of animaginary sample supporting it. Increasing the ISS will increase the density of the estimated graph.

Details

The class implements a score which scores all edges contradicting the ordering (edge going from a later tier to an earlier) to minus \infty. If the the edges does not contradict, the score is equal to that of the standard BDeu.

Extends

Class pcalg::Score directly.

All reference classes extend and inherit methods from envRefClass.

Constructor

new("TemporalBdeu",
  data = matrix(1, 1, 1),
  order =  rep(1,ncol(data)),
  iss = 1
  ...)

Alternative implementation of TemporalBDeu score

We provide here a faster alternative to the implemented version of TemporalBDeu. However, this version relies on a non-exporten function from bnlearn. We provide the code for it here:

setRefClass("TemporalBDeu",
            contains = "DataScore",

            fields = list(
              .order = "vector",
              .iss = "numeric"),

            methods = list(
              initialize = function(data = matrix(1, 1, 1),
                                    nodes = colnames(data),
                                    iss = 1,
                                    order = rep(1,ncol(data)),
                                    ...) {
                .order <<- order
                .iss <<- iss
                callSuper(data = data,
                          nodes = nodes,
                          iss = iss,
                          ...)},


              local.score = function(vertex, parents,...) {
                ## Check validity of arguments
                validate.vertex(vertex)
                validate.parents(parents)
                order <- .order
                iss <- .iss
                if (order[vertex] >= max(c(order[parents],-Inf))){
                  #Checks if the tier of parents are before or same as node

                  # Create local dataset
                  D <- pp.dat$data
                  pa_nam <- colnames(D)[parents]
                  ve_nam <- colnames(D)[vertex]
                  res_nam <- colnames(D)[-c(parents,vertex)]


                  # Create local bn object
                  if (length(parents) > 0){
                    mod_string <- paste(c("[",
                                          paste(c(pa_nam,res_nam),collapse = "]["),
                                          "][",
                                          ve_nam,
                                          "|",
                                          paste(pa_nam,collapse = ":"),
                                          "]"
                    ),collapse = "")

                  } else {
                    mod_string <- paste(c("[",
                                          paste(c(pa_nam,res_nam),collapse = "]["),
                                          "][",
                                          ve_nam,
                                          "]"
                    ),collapse = "")
                  }

                  bn_ob <- as.bn(mod_string)
                  BdeuScore <- bnlearn:::per.node.score(network = bn_ob,
                                                        data = D,
                                                        score = "bde",
                                                        targets = ve_nam,
                                                        extra.args = list(iss = iss,
                                                                          prior = "uniform"))
                  return(BdeuScore)
                }
                else { skip <- -Inf
                return(skip)}#set score to minus infinity if vertex earlier than parents
              }),
            inheritPackage = TRUE
)

Author(s)

Tobias Ellegaard Larsen

See Also

tges

Examples

# For reproducibility
set.seed(123)

# Number of samples
n <- 1000

# Define probabilities for A
p_A <- c(0.4, 0.35, 0.25)  # Probabilities for A = {1, 2, 3}

# Simulate A from a categorical distribution
A <- sample(1:3, n, replace = TRUE, prob = p_A)

# Define conditional probabilities for B given A
p_B_given_A <- list(
  c(0.7, 0.3),  # P(B | A=1)
  c(0.4, 0.6),  # P(B | A=2)
  c(0.2, 0.8)   # P(B | A=3)
)

# Sample B based on A
B <- sapply(A, function(a) sample(1:2, 1, prob = p_B_given_A[[a]]))

# Define conditional probabilities for C given A and B
p_C_given_A_B <- list(
  "1_1" = c(0.6, 0.4),  # P(C | A=1, B=1)
  "1_2" = c(0.3, 0.7),  # P(C | A=1, B=2)
  "2_1" = c(0.5, 0.5),  # P(C | A=2, B=1)
  "2_2" = c(0.2, 0.8),  # P(C | A=2, B=2)
  "3_1" = c(0.7, 0.3),  # P(C | A=3, B=1)
  "3_2" = c(0.4, 0.6)   # P(C | A=3, B=2)
)

# Sample C based on A and B
C <- mapply(function(a, b) sample(1:2, 1, prob = p_C_given_A_B[[paste(a, b, sep = "_")]]), A, B)

# Create dataset
simdata <- data.frame(as.factor(A), as.factor(B), as.factor(C))

# Define order in prefix way
colnames(simdata) <- c("child_A","child_B","adult_C")
prefix_order <- c("child", "adult")

# Define TemporalBDeu score
t_score <- new("TemporalBDeu", order = prefix_order
               , data = simdata)
# Run tges
tges_pre <- tges(t_score)

# Plot MPDAG
plot(tges_pre)


# Define order in integer way
colnames(simdata) <- c("A","B","C")
integer_order <- c(1,1,2)

# Define TemporalBDeu score
t_score <- new("TemporalBDeu", order = integer_order
               , data = simdata)
# Run tges
tges_integer <- tges(t_score)

# Plot MPDAG
plot(tges_integer)

causalDisco documentation built on Jan. 20, 2026, 5:09 p.m.