Tlearners: T-Learners
In soerenkuenzel/causalToolbox: Toolbox for Causal Inference with emphasize on Heterogeneous Treatment Effect Estimator

Description Usage Arguments Details Value References See Also Examples

T_RF is an implementation of the T-learner combined with Random Forest (Breiman 2001) for both response functions.

T_BART is an implementation of the T-learner with Bayesian Additive Regression Trees (Chipman et al. 2010) for both response functions.

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))

`feat`	A data frame containing the features.
`tr`	A numeric vector with 0 for control and 1 for treated variables.
`yobs`	A numeric vector containing the observed outcomes.
`nthread`	Number of threads which should be used to work in parallel.
`verbose`	TRUE for detailed output, FALSE for no output.
`mu0.forestry, mu1.forestry`	Lists containing the hyperparameters for the `forestry` package that are used in \hat μ_0 and \hat μ_1, respectively. These hyperparameters are passed to the `forestry` package. (Please refer to the `forestry` package for a more detailed documentation of the hyperparamters.)
`ndpost`	Number of posterior draws.
`ntree`	Number of trees.
`mu0.BART, mu1.BART`	Hyperparameters of the BART functions for the control and treated group. (Use `?BART::mc.wbart` for a detailed explanation of their effects.)

The CATE is estimated using two estimators:

Estimate the response functions

μ_0(x) = E[Y(0) | X = x]

μ_1(x) = E[Y(1) | X = x]

using the base leaner and denote the estimates as \hat μ_0 and \hat μ_1.
Define the CATE estimate as

τ(x) = \hat μ_1 - \hat μ_0.

Object of class T_RF. It should be used with one of the following functions EstimateCATE, CateCI, and CateBIAS. The object has the following slots:

`feature_train`	A copy of feat.
`tr_train`	A copy of tr.
`yobs_train`	A copy of yobs.
`m_0`	An object of class forestry that is fitted with the observed outcomes of the control group as the dependent variable.
`m_1`	An object of class forestry that is fitted with the observed outcomes of the treated group as the dependent variable.
`hyperparameter_list`	List containting the hyperparameters of the three random forest algorithms used.
`creator`	Function call of T_RF. This is used for different bootstrap procedures.

Sören Künzel, Jasjeet Sekhon, Peter Bickel, and Bin Yu (2017). MetaLearners for Estimating Heterogeneous Treatment Effects using Machine Learning. https://www.pnas.org/content/116/10/4156
Sören Künzel, Simon Walter, and Jasjeet Sekhon (2018). Causaltoolbox—Estimator Stability for Heterogeneous Treatment Effects. https://arxiv.org/pdf/1811.02833.pdf
Sören Künzel, Bradly Stadie, Nikita Vemuri, Varsha Ramakrishnan, Jasjeet Sekhon, and Pieter Abbeel (2018). Transfer Learning for Estimating Causal Effects using Neural Networks. https://arxiv.org/pdf/1808.07804.pdf

Other metalearners: M-Learner, S-Learner, X-Learner

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)
## Not run: 
# create confidence intervals via bootstrapping. 
xl_ci_rf <- CateCI(xl_rf, feature_test, B = 500)

## End(Not run)