Skip to contents

Uses a pre-trained ARF model to estimate leaf and distribution parameters.


  oob = FALSE,
  family = "truncnorm",
  finite_bounds = c("no", "local", "global"),
  alpha = 0,
  epsilon = 0,
  parallel = TRUE



Pre-trained adversarial_rf. Alternatively, any object of class ranger.


Training data for estimating parameters.


Only use out-of-bag samples for parameter estimation? If TRUE, x must be the same dataset used to train arf.


Distribution to use for density estimation of continuous features. Current options include truncated normal (the default family = "truncnorm") and uniform (family = "unif"). See Details.


Impose finite bounds on all continuous variables? If 'local', infinite bounds are shrinked to empirical extrema within leaves. If 'global', infinite bounds are shrinked to global empirical extrema. if 'no' (the default), do not impose finite bounds.


Optional pseudocount for Laplace smoothing of categorical features. This avoids zero-mass points when test data fall outside the support of training data. Effectively parametrizes a flat Dirichlet prior on multinomial likelihoods.


Optional slack parameter on empirical bounds when finite_bounds != 'no'. This avoids zero-density points when test data fall outside the support of training data. The gap between lower and upper bounds is expanded by a factor of 1 + epsilon.


Compute in parallel? Must register backend beforehand, e.g. via doParallel.


A list with 5 elements: (1) parameters for continuous data; (2) parameters for discrete data; (3) leaf indices and coverage; (4) metadata on variables; and (5) the data input class. This list is used for estimating likelihoods with lik and generating data with forge.


forde extracts leaf parameters from a pretrained forest and learns distribution parameters for data within each leaf. The former includes coverage (proportion of data falling into the leaf) and split criteria. The latter includes proportions for categorical features and mean/variance for continuous features. The result is a probabilistic circuit, stored as a data.table, which can be used for various downstream inference tasks.

Currently, forde only provides support for a limited number of distributional families: truncated normal or uniform for continuous data, and multinomial for discrete data. Future releases will accommodate a larger set of options.

Though forde was designed to take an adversarial random forest as input, the function's first argument can in principle be any object of class ranger. This allows users to test performance with alternative pipelines (e.g., with supervised forest input). There is also no requirement that x be the data used to fit arf, unless oob = TRUE. In fact, using another dataset here may protect against overfitting. This connects with Wager & Athey's (2018) notion of "honest trees".


Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests for density estimation and generative modeling. In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, pp. 5357-5375.

Wager, S. & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc., 113(523): 1228-1242.


# Train ARF and estimate leaf parameters
arf <- adversarial_rf(iris)
#> Iteration: 0, Accuracy: 88.22%
#> Iteration: 1, Accuracy: 46.62%
psi <- forde(arf, iris)

# Generate 100 synthetic samples from the iris dataset
x_synth <- forge(psi, n_synth = 100)

# Condition on Species = "setosa" and Sepal.Length > 6
evi <- data.frame(Species = "setosa",
                  Sepal.Length = "(6, Inf)")
x_synth <- forge(psi, n_synth = 100, evidence = evi)

# Estimate average log-likelihood
ll <- lik(psi, iris, arf = arf, log = TRUE)
#> [1] -0.761797

# Expectation of Sepal.Length for class setosa
evi <- data.frame(Species = "setosa")
expct(psi, query = "Sepal.Length", evidence = evi)
#>   Sepal.Length
#> 1            5