SurvivalBCF: SurvivalBCF (Bayesian Causal Forest for survival data)

Description

Fits an Accelerated Failure Time (AFT) version of Bayesian Causal Forest (BCF): \(Y = \mu(x) + W \tau(x) + \varepsilon\), where separate forests are used for the prognostic (control) function \(\mu(x)\) and the treatment effect function \(\tau(x)\).

Usage

SurvivalBCF(
  time,
  status,
  X_train,
  treatment,
  timescale = "time",
  propensity = NULL,
  number_of_trees_control = 200,
  number_of_trees_treat = 50,
  power_control = 2,
  base_control = 0.95,
  power_treat = 3,
  base_treat = 0.25,
  N_post = 1000,
  N_burn = 1000,
  verbose = TRUE,
  ...
)

Arguments

time: Outcome vector of right-censored (non-negative) survival times.
status: Event indicator (1 = event, 0 = censored).
X_train: Design matrix for training data.
treatment: Treatment indicator (0/1) for training data.
timescale: Either "time" (log-transform internally) or "log" (already log-transformed).
propensity: Optional vector of propensity scores. If provided, it is appended to the control forest design matrix.
number_of_trees_control: Number of trees in the control forest. Default is 200.
number_of_trees_treat: Number of trees in the treatment forest. Default is 50.
power_control, base_control: Tree-structure prior parameters for the control forest.
power_treat, base_treat: Tree-structure prior parameters for the treatment forest.
N_post: Number of posterior samples to store.
N_burn: Number of burn-in iterations.
verbose: Logical; print sampling progress.
...: Additional arguments passed to CausalShrinkageForest to override default hyperparameters.

Details

This wrapper provides a survival-specific implementation using classical BART-style priors for both forests.

This function implements a simplified AFT-BCF model for right-censored survival outcomes. Structural regularisation is induced through classical BART priors on the tree structure and leaf parameters.

Users requiring alternative shrinkage priors (e.g., Horseshoe or Dirichlet splitting priors) should use SurvivalShrinkageBCF or call CausalShrinkageForest directly.

References

Hahn, P. R., Murray, J. S., & Carvalho, C. M. (2020). Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects. Bayesian Analysis.