CIMTx (version 0.3.0)

bart_multiTrt_ate: Bayesian Additive Regression Trees (BART) for ATE estimation

Description

This function implements the BART method when estimand is ATE. Please use our main function causal_multi_treat.R.

Usage

bart_multiTrt_ate(
  y,
  x,
  trt,
  k = 2,
  discard = "No",
  ntree = 100,
  ndpost = parent.frame()$ndpost,
  nskip = 1000
)

Arguments

y

numeric vector for the binary outcome

x

dataframe including the treatment indicator and the covariates

trt

numeric vector for the treatment indicator

k

For binary y, k is the number of prior standard deviations f(x) is away from +/-3. The bigger k is, the more conservative the fitting will be.

discard

discarding rules for BART method, inherited from causal_multi_treat.R

ntree

The number of trees in the sum

ndpost

The number of posterior draws returned

nskip

Number of MCMC iterations to be treated as burn in

Value

list with 2 elements for ATT effect. It contains

ATT12:

A dataframe containing the estimation, standard error, lower and upper 95% CI for RD/RR/OR

ATT13:

A dataframe containing the estimation, standard error, lower and upper 95% CI for RD/RR/OR

list with 3 elements for ATE effect. It contains

ATE12:

A dataframe containing the estimation, standard error, lower and upper 95% CI for RD/RR/OR

ATE13:

A dataframe containing the estimation, standard error, lower and upper 95% CI for RD/RR/OR

ATE23:

A dataframe containing the estimation, standard error, lower and upper 95% CI for RD/RR/OR

Examples

# NOT RUN {
library(CIMTx)
set.seed(3242019)
idata = data_gen(n = 3, ratio =1,scenario = 1)
trt_ind <- as.numeric(idata$trtdat$trt_ind)
all_vars <- idata$trtdat[, -1] #exclude treatment indicator
y <- idata$Yobs
causal_multi_treat(y = y, x = idata$trtdat,
trt = trt_ind, method = "BART", estimand = "ATE", discard = "No", ndpost = 10)
# }