teacher_models: Teacher models for causal distillation trees

Description

These functions are wrappers around various heterogeneous treatment effect learners that can be easily used as teacher models in the causal distillation tree framework.

causal_forest(): wrapper around grf::causal_forest().
bcf(): wrapper around bcf::bcf().
rboost(): (defunct) wrapper around rlearner::rboost().
rlasso(): (defunct) wrapper around rlearner::rlasso().
rkern(): (defunct) wrapper around rlearner::rkern().

Warning: The rboost(), rlasso(), and rkern() functions are defunct as of version 1.0.0. Use rlearner_teacher() (e.g., rlearner_teacher(rlearner::rboost)) instead to convert rlearner functions into correct format for use as teacher model in CDT.

Usage

causal_forest(X, Y, Z, W = NULL, ...)
rboost(X, Y, Z, W = NULL, ...)
rlasso(X, Y, Z, W = NULL, ...)
rkern(X, Y, Z, W = NULL, ...)
bcf(
  X,
  Y,
  Z,
  W = NULL,
  pihat = "default",
  w = NULL,
  nburn = 2000,
  nsim = 1000,
  n_threads = 1,
  no_output = TRUE,
  ...
)

Value

Outputs of the respective base model functions:

causal_forest(): see output of grf::causal_forest().
rboost() (defunct): see output of rlearner::rboost().
rlasso() (defunct): see output of rlearner::rlasso().
rkern() (defunct): see output of rlearner::rkern().

Arguments

X: A tibble, data.frame, or matrix of covariates.
Y: A vector of outcomes.
Z: A vector of treatments.
W: A vector of weights corresponding to treatment propensities.
...: Additional arguments to pass to the base model functions.
pihat: Length n estimates of propensity score
w: An optional vector of weights. When present, BCF fits a model \(y | x ~ N(f(x), \sigma^2 / w)\), where \(f(x)\) is the unknown function.
nburn: Number of burn-in MCMC iterations
nsim: Number of MCMC iterations to save after burn-in. The chain will run for nsim*nthin iterations after burn-in
n_threads: An optional integer of the number of threads to parallelize within chain bcf operations on
no_output: logical, whether to suppress writing trees and training log to text files, defaults to FALSE.