compute_bcf_posterior_interval: Compute posterior credible intervals for BCF model terms

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

Compute posterior credible intervals for BCF model terms

Description

Compute posterior credible intervals for specified terms from a fitted BCF model. Supports intervals for prognostic forests, CATE forests, variance forests, random effects, and overall mean outcome predictions.

Usage

compute_bcf_posterior_interval(
  model_object,
  terms,
  level = 0.95,
  scale = "linear",
  X = NULL,
  Z = NULL,
  propensity = NULL,
  rfx_group_ids = NULL,
  rfx_basis = NULL
)

Arguments

model_object	A fitted BCF model object of class bcfmodel.
terms	A character string specifying the model term(s) for which to compute intervals. Options for BCF models are "prognostic_function", "mu", "cate", "tau", "variance_forest", "rfx", or "y_hat". Note that "mu" is only different from "prognostic_function" if random effects are included with a model spec of "intercept_only" or "intercept_plus_treatment" and "tau" is only different from "cate" if random effects are included with a model spec of "intercept_plus_treatment".
level	A numeric value between 0 and 1 specifying the credible interval level (default is 0.95 for a 95% credible interval).
scale	(Optional) Scale of mean function predictions. Options are "linear", which returns predictions on the original scale of the mean forest / RFX terms, and "probability", which transforms predictions into a probability of observing y == 1. "probability" is only valid for models fit with a probit outcome model. Default: "linear".
X	(Optional) A matrix or data frame of covariates at which to compute the intervals. Required if the requested term depends on covariates (e.g., prognostic forest, CATE forest, variance forest, or overall predictions).
Z	(Optional) A vector or matrix of treatment assignments. Required if the requested term is "y_hat" (overall predictions).
propensity	(Optional) A vector or matrix of propensity scores. Required if the underlying model depends on user-provided propensities.
rfx_group_ids	An optional vector of group IDs for random effects. Required if the requested term includes random effects.
rfx_basis	An optional matrix of basis function evaluations for random effects. Required if the requested term includes random effects.

Value

A list containing the lower and upper bounds of the credible interval for the specified term. If multiple terms are requested, a named list with intervals for each term is returned.

Examples

n <- 100
p <- 5
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
pi_X <- pnorm(0.5 * X[,1])
Z <- rbinom(n, 1, pi_X)
mu_X <- X[,1]
tau_X <- 0.25 * X[,2]
y <- mu_X + tau_X * Z + rnorm(n)
bcf_model <- bcf(X_train = X, Z_train = Z, y_train = y,
                 propensity_train = pi_X)
intervals <- compute_bcf_posterior_interval(
 model_object = bcf_model,
 terms = c("prognostic_function", "cate"),
 X = X,
 Z = Z,
 propensity = pi_X,
 level = 0.90
)

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