sample_bcf_posterior_predictive: Sample from the posterior predictive distribution for...

In stochtree: Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference

Sample from the posterior predictive distribution for outcomes modeled by BCF

Description

Sample from the posterior predictive distribution for outcomes modeled by BCF

Usage

sample_bcf_posterior_predictive(
  model_object,
  X = NULL,
  Z = NULL,
  propensity = NULL,
  rfx_group_ids = NULL,
  rfx_basis = NULL,
  num_draws_per_sample = NULL
)

Arguments

model_object	A fitted BCF model object of class bcfmodel.
X	A matrix or data frame of covariates.
Z	A vector or matrix of treatment assignments.
propensity	(Optional) A vector or matrix of propensity scores. Required if the underlying model depends on user-provided propensities.
rfx_group_ids	(Optional) A vector of group IDs for random effects model. Required if the BCF model includes random effects.
rfx_basis	(Optional) A matrix of bases for random effects model. Required if the BCF model includes random effects.
num_draws_per_sample	(Optional) The number of samples to draw from the likelihood for each draw of the posterior. Defaults to a heuristic based on the number of samples in a BCF model (i.e. if the BCF model has >1000 draws, we use 1 draw from the likelihood per sample, otherwise we upsample to ensure at least 1000 posterior predictive draws).

Value

Array of posterior predictive samples with dimensions (num_observations, num_posterior_samples, num_draws_per_sample) if num_draws_per_sample > 1, otherwise (num_observations, num_posterior_samples).

Examples

n <- 100
p <- 5
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
pi_X <- pnorm(X[,1] / 2)
Z <- rbinom(n, 1, pi_X)
y <- 2 * X[,2] + 0.5 * X[,2] * Z + rnorm(n)
bcf_model <- bcf(X_train = X, Z_train = Z, y_train = y, propensity_train = pi_X)
ppd_samples <- sample_bcf_posterior_predictive(
  model_object = bcf_model, X = X,
  Z = Z, propensity = pi_X
)

stochtree documentation built on Nov. 22, 2025, 9:06 a.m.