This package is an implementation of a C++ sampler that uses BART for non-parametric mean components and Stan for multilevel/parametric ones.
- Install the developer tools for your platform (Mac OS, Windows). Mac OS users will need the (linked) gfortan for their respective platforms.
- Execute:
if (length(find.package("remotes", quiet = TRUE)) == 0L)
install.packages("remotes")
remotes::install_github("vdorie/dbarts")
remotes::install_github("vdorie/stan4bart")
The package utilizes the flexible, expressive lme4 syntax for specifying group-level structures. See the package documentation ?stan4bart and ?stan4bart::stan4bart-generics for more information.
y ~ bart(x_nuisance_1 + x_nuisance_1) + x_inference- wrap the formula for any variables that you want to be fit non-parametrically in a call tobart();+s withinbart()are interpretted figuratively as indicating the names of the variables to be includedy ~ bart(x_fixef) + (1 + x_ranef | g)- supports alllme4varying intercept and slope formula constructsy ~ x_1 + (1 + x_2 | g)- if there's no BART component, userstanarmorlme4instead
The main function is stan4bart.
Results are retrieved using the extract, fitted, and predict generics. See ?"stan4bart-generics" for more information.
The name and definition of extract conflict with rstan. The rstan package is not needed to use stan4bart and does not need to be loaded. If a name-collision occurs, the stan4bart extract can be referenced as in:
stan4bart:::extract.stan4bartFit(stan4bart_fit)library(stan4bart)
# Load a test-data function
source(system.file("common", "friedmanData.R", package = "stan4bart"), local = TRUE)
# Relatively low n for illustrative purposes
testData <- generateFriedmanData(n = 100, ranef = TRUE, causal = TRUE, binary = FALSE)
# First level model is:
# y ~ f(x_1, x_2) + a * x_3^2 + b * x_4 + c * x_5 + z
# Random intercepts are added for g.1 and g.2, and a random slope is placed on x4
# x_6 through x_10 are pure noise
df <- with(testData, data.frame(x, g.1, g.2, y, z))
# Causal inference example
fit <- stan4bart(y ~ bart(. - g.1 - g.2 - X4 - z) + X4 + z + (1 + X4 | g.1) + (1 | g.2), df,
treatment = z,
cores = 1, seed = 0,
verbose = 1)
samples.mu.train <- extract(fit)
samples.mu.test <- extract(fit, sample = "test")
# Individual conditional treatment effects
samples.icate <- (samples.mu.train - samples.mu.test) * (2 * testData$z - 1)
# Conditional average treatment effect
samples.cate <- apply(samples.icate, 2, mean)
cate <- mean(samples.cate)
cate.int <- c(cate - 1.96 * sd(samples.cate), cate + 1.96 * sd(samples.cate))
# Samples of the posterior predictive distribution are used in calculating
# the counterfactuals for SATE and for calculating the response under
# the observed treatment condition when estimating PATE.
samples.ppd.test <- extract(fit, type = "ppd", sample = "test")
# Individual sample treatment effects
samples.ite <- (testData$y - samples.ppd.test) * (2 * testData$z - 1)
# Sample average treatment effect
samples.sate <- apply(samples.ite, 2, mean)
sate <- mean(samples.sate)
sate.int <- c(sate - 1.96 * sd(samples.sate), sate + 1.96 * sd(samples.sate))
# Population average treatment effect
samples.ppd.train <- extract(fit, type = "ppd", sample = "train")
samples.pate <- apply((samples.ppd.train - samples.ppd.test) * (2 * testData$z - 1), 2, mean)
pate <- mean(samples.pate)
pate.int <- c(pate - 1.96 * sd(samples.pate), pate + 1.96 * sd(samples.pate))
fitted.mu.train <- fitted(fit)
# equal to: apply(samples.mu.train, 1, mean)
fitted.mu.test <- fitted(fit, sample = "test")
# equal to: apply(samples.mu.test, 1, mean)
# Observed and conterfactual MSE
mse.train <- with(testData, mean((fitted.mu.train - mu.1 * z - mu.0 * (1 - z))^2))
mse.test <- with(testData, mean((fitted.mu.test - mu.1 * (1 - z) - mu.0 * z)^2))
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