% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TRF.R, R/TBART.R
\name{T-Learner}
\alias{T-Learner}
\alias{T_RF}
\alias{T_BART}
\title{T-Learners}
\usage{
T_RF(feat, tr, yobs, nthread = 0, verbose = TRUE,
  mu0.forestry = list(relevant.Variable = 1:ncol(feat), ntree = 1000,
  replace = TRUE, sample.fraction = 0.9, mtry = ncol(feat), nodesizeSpl =
  1, nodesizeAvg = 3, splitratio = 0.5, middleSplit = FALSE),
  mu1.forestry = list(relevant.Variable = 1:ncol(feat), ntree = 1000,
  replace = TRUE, sample.fraction = 0.9, mtry = ncol(feat), nodesizeSpl =
  1, nodesizeAvg = 3, splitratio = 0.5, middleSplit = FALSE))

T_BART(feat, tr, yobs, ndpost = 1200, ntree = 200, nthread = 1,
  verbose = FALSE, mu0.BART = list(sparse = FALSE, theta = 0, omega =
  1, a = 0.5, b = 1, augment = FALSE, rho = NULL, usequants = FALSE, cont =
  FALSE, sigest = NA, sigdf = 3, sigquant = 0.9, k = 2, power = 2, base =
  0.95, sigmaf = NA, lambda = NA, numcut = 100L, nskip = 100L),
  mu1.BART = list(sparse = FALSE, theta = 0, omega = 1, a = 0.5, b = 1,
  augment = FALSE, rho = NULL, usequants = FALSE, cont = FALSE, sigest =
  NA, sigdf = 3, sigquant = 0.9, k = 2, power = 2, base = 0.95, sigmaf =
  NA, lambda = NA, numcut = 100L, nskip = 100L))
}
\arguments{
\item{feat}{A data frame containing the features.}

\item{tr}{A numeric vector with 0 for control and 1 for treated variables.}

\item{yobs}{A numeric vector containing the observed outcomes.}

\item{nthread}{Number of threads which should be used to work in parallel.}

\item{verbose}{TRUE for detailed output, FALSE for no output.}

\item{mu0.forestry, mu1.forestry}{Lists containing the hyperparameters for the
\code{forestry} package that are used in \eqn{\hat \mu_0} and \eqn{\hat
\mu_1}, respectively. These hyperparameters are passed to the
\code{forestry} package. (Please refer to the
\href{https://github.com/soerenkuenzel/forestry}{\code{forestry}} package
for a more detailed documentation of the hyperparamters.)}

\item{ndpost}{Number of posterior draws.}

\item{ntree}{Number of trees.}

\item{mu0.BART, mu1.BART}{Hyperparameters of the BART functions for the
control and treated group. (Use \code{?BART::mc.wbart} for a detailed 
explanation of their effects.)}
}
\value{
Object of class \code{T_RF}. It should be used with one of the
  following functions \code{EstimateCATE}, \code{CateCI}, and
  \code{CateBIAS}. The object has the following slots:
  \item{\code{feature_train}}{A copy of feat.}
  \item{\code{tr_train}}{A copy of tr.}
  \item{\code{yobs_train}}{A copy of yobs.}
  \item{\code{m_0}}{An object of class forestry that is fitted with the 
     observed outcomes of the control group as the dependent variable.}
  \item{\code{m_1}}{An object of class forestry that is fitted with the 
     observed outcomes of the treated group as the dependent variable.}
  \item{\code{hyperparameter_list}}{List containting the hyperparameters of 
     the three random forest algorithms used.}
  \item{\code{creator}}{Function call of T_RF. This is used for different 
     bootstrap procedures.}
}
\description{
\code{T_RF} is an implementation of the T-learner combined with Random
  Forest (Breiman 2001) for both response functions.

\code{T_BART} is an implementation of the T-learner with Bayesian
  Additive Regression Trees (Chipman et al. 2010) for both response
  functions.
}
\details{
The CATE is estimated using two estimators:
\enumerate{
 \item
    Estimate the response functions 
    \deqn{\mu_0(x) = E[Y(0) | X = x]}
    \deqn{\mu_1(x) = E[Y(1) | X = x]} 
    using the base leaner and denote the estimates as \eqn{\hat \mu_0} and
    \eqn{\hat \mu_1}.
 \item 
    Define the CATE estimate as
    \deqn{\tau(x) = \hat \mu_1 - \hat \mu_0.}
}
}
\examples{
require(causalToolbox)

# create example data set
simulated_experiment <- simulate_causal_experiment(
  ntrain = 1000,
  ntest = 1000,
  dim = 10
)
feat <- simulated_experiment$feat_tr
tr <- simulated_experiment$W_tr
yobs <- simulated_experiment$Yobs_tr
feature_test <- simulated_experiment$feat_te

# create the CATE estimator using Random Forests (RF)
xl_rf <- X_RF(feat = feat, tr = tr, yobs = yobs)
tl_rf <- T_RF(feat = feat, tr = tr, yobs = yobs)
sl_rf <- S_RF(feat = feat, tr = tr, yobs = yobs)
ml_rf <- M_RF(feat = feat, tr = tr, yobs = yobs)
xl_bt <- X_BART(feat = feat, tr = tr, yobs = yobs)
tl_bt <- T_BART(feat = feat, tr = tr, yobs = yobs)
sl_bt <- S_BART(feat = feat, tr = tr, yobs = yobs)
ml_bt <- M_BART(feat = feat, tr = tr, yobs = yobs)
  
cate_esti_xrf <- EstimateCate(xl_rf, feature_test)

# evaluate the performance.
cate_true <- simulated_experiment$tau_te
mean((cate_esti_xrf - cate_true) ^ 2)
\dontrun{
# create confidence intervals via bootstrapping. 
xl_ci_rf <- CateCI(xl_rf, feature_test, B = 500)
}
}
\references{
\itemize{
  \item Sören Künzel, Jasjeet Sekhon, Peter Bickel, and Bin Yu (2017). 
    MetaLearners for Estimating Heterogeneous Treatment Effects using
    Machine Learning. 
    \url{https://www.pnas.org/content/116/10/4156}
  \item 
    Sören Künzel, Simon Walter, and Jasjeet Sekhon (2018).
    Causaltoolbox---Estimator Stability for Heterogeneous Treatment Effects.
    \url{https://arxiv.org/pdf/1811.02833.pdf}
  \item Sören Künzel, Bradly Stadie, Nikita Vemuri, Varsha Ramakrishnan, 
    Jasjeet Sekhon, and Pieter Abbeel (2018). 
    Transfer Learning for Estimating Causal Effects using Neural Networks. 
    \url{https://arxiv.org/pdf/1808.07804.pdf}
  }
}
\seealso{
Other metalearners: \code{\link{M-Learner}},
  \code{\link{S-Learner}}, \code{\link{X-Learner}}
}
\concept{metalearners}
