bart {bartcs}  R Documentation 
Fit Bayesian Regression Additive Trees (BART) models
to select relevant confounders among a large set of potential confounders
and to estimate average treatment effect E[Y(1)  Y(0)]
.
separate_bart(
Y, trt, X,
trt_treated = 1,
trt_control = 0,
num_tree = 50,
num_chain = 4,
num_burn_in = 100,
num_thin = 1,
num_post_sample = 100,
step_prob = c(0.28, 0.28, 0.44),
alpha = 0.95,
beta = 2,
nu = 3,
q = 0.95,
dir_alpha = 5,
parallel = FALSE,
verbose = TRUE
)
single_bart(
Y, trt, X,
trt_treated = 1,
trt_control = 0,
num_tree = 50,
num_chain = 4,
num_burn_in = 100,
num_thin = 1,
num_post_sample = 100,
step_prob = c(0.28, 0.28, 0.44),
alpha = 0.95,
beta = 2,
nu = 3,
q = 0.95,
dir_alpha = 5,
parallel = FALSE,
verbose = TRUE
)
Y 
A vector of outcome values. 
trt 
A vector of treatment values. Binary treatment works for both model and continuous treatment works for single_bart(). For binary treatment, use 1 to indicate the treated group and 0 for the control group. 
X 
A matrix of potential confounders. 
trt_treated 
Value of 
trt_control 
Value of 
num_tree 
Number of trees in BART model. The default value is set to 100. 
num_chain 
Number of MCMC chains.
Need to set 
num_burn_in 
Number of MCMC samples to be discarded per chain as initial burnin periods. The default value is set to 100. 
num_thin 
Number of thinning per chain.
One in every 
num_post_sample 
Final number of posterior samples per chain.
Number of MCMC iterations per chain is

step_prob 
A vector of tree alteration probabilities (GROW, PRUNE, CHANGE).
Each alteration is proposed to change the tree structure. 
alpha , beta 
Hyperparameters for tree regularization prior.
A terminal node of depth 
nu , q 
Values to calibrate hyperparameter of sigma prior. 
dir_alpha 
Hyperparameter of Dirichlet prior for selection probabilities. The default value is 5. 
parallel 
If 
verbose 
If 
separate_bart()
and single_bart()
fit an exposure model and outcome model(s)
for estimating treatment effect with adjustment of confounders
in the presence of a large set of potential confounders (Kim et al. 2023).
The exposure model E[AX]
and the outcome model(s) E[YA,X]
are
linked together with a common Dirichlet prior that accrues
posterior selection probabilities to the corresponding confounders (X
)
on the basis of association with both the exposure (A
) and the
outcome (Y
).
There is a distinction between fitting separate outcome models for the treated and control groups and fitting a single outcome model for both groups.
separate_bart()
specifies two "separate" outcome models
for two binary treatment levels.
Thus, it fits three models:
one exposure model and two separate outcome models for A = 0, 1
.
single_bart()
specifies one "single" outcome model.
Thus, it fits two models:
one exposure model and one outcome model for the entire sample.
All inferences are made with outcome model(s).
A bartcs
object. A list object contains the following components.
mcmc_list 
A 
ATE
Posterior sample of average treatment effect E[Y(1)  Y(0)]
.
Y1
Posterior sample of potential outcome E[Y(1)]
.
Y0
Posterior sample of potential outcome E[Y(0)]
.
dir_alpha
Posterior sample of dir_alpha.
sigma2_out
Posterior sample of sigma2
in the outcome model.
var_prob 
Aggregated posterior inclusion probability of each variable. 
var_count 
Number of selection of each variable in each MCMC iteration.
Its dimension is 
chains 
A list of results from each MCMC chain. 
model 

label 
Column names of 
params 
Parameters used in the model. 
Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266298. doi:10.1214/09AOAS285
Kim, C., Tec, M., & Zigler, C. M. (2023). Bayesian Nonparametric Adjustment of Confounding, Biometrics doi:10.1111/biom.13833
data(ihdp, package = "bartcs")
single_bart(
Y = ihdp$y_factual,
trt = ihdp$treatment,
X = ihdp[, 6:30],
num_tree = 10,
num_chain = 2,
num_post_sample = 20,
num_burn_in = 10,
verbose = FALSE
)
separate_bart(
Y = ihdp$y_factual,
trt = ihdp$treatment,
X = ihdp[, 6:30],
num_tree = 10,
num_chain = 2,
num_post_sample = 20,
num_burn_in = 10,
verbose = FALSE
)