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

T-Learner

R Documentation

T-Learners

Description

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.

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,
    nodesizeStrictSpl = 1, nodesizeStrictAvg = 1, splitratio = 1, middleSplit = FALSE,
    OOBhonest = TRUE),
  mu1.forestry = list(relevant.Variable = 1:ncol(feat), ntree = 1000, replace = TRUE,
    sample.fraction = 0.9, mtry = ncol(feat), nodesizeSpl = 1, nodesizeAvg = 3,
    nodesizeStrictSpl = 1, nodesizeStrictAvg = 1, splitratio = 1, middleSplit = FALSE,
    OOBhonest = TRUE)
)

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

`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.)

Details

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.

Value

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 containing the hyperparameters of the three random forest algorithms used.
`creator`	Function call of T_RF. This is used for different bootstrap procedures.

Author(s)

Soeren R. Kuenzel

References

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

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

## End(Not run)

forestry-labs/causalToolbox documentation built on Feb. 6, 2023, 11:27 p.m.