Package 'bartcs' reference manual

Title:	Bayesian Additive Regression Trees for Confounder Selection
Description:	Fit Bayesian Regression Additive Trees (BART) models to select true confounders from a large set of potential confounders and to estimate average treatment effect. For more information, see Kim et al. (2023) <doi:10.1111/biom.13833>.
Authors:	Yeonghoon Yoo [aut, cre]
Maintainer:	Yeonghoon Yoo <[email protected]>
License:	GPL (>= 3)
Version:	1.2.2
Built:	2024-11-28 06:48:36 UTC
Source:	CRAN

Fit BART models to select confounders and estimate treatment effect

Description

Fit Bayesian Regression Additive Trees (BART) models to select relevant confounders among a large set of potential confounders and to estimate average treatment effect $E[Y(1) - Y(0)]$ .

Usage

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
)
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
)

Arguments

`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` for the treated group. The default value is set to 1.
`trt_control`	Value of `trt` for the control group. The default value is set to 0.
`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_chain > 1` for the Gelman-Rubin diagnostic. The default value is set to 4.
`num_burn_in`	Number of MCMC samples to be discarded per chain as initial burn-in periods. The default value is set to 100.
`num_thin`	Number of thinning per chain. One in every `num_thin` samples are selected. The default value is set to 1.
`num_post_sample`	Final number of posterior samples per chain. Number of MCMC iterations per chain is `burn_in + num_thin * num_post_sample`. The default value is set to 100.
`step_prob`	A vector of tree alteration probabilities (GROW, PRUNE, CHANGE). Each alteration is proposed to change the tree structure. The default setting is `⁠(0.28, 0.28, 0.44)⁠`.
`alpha`, `beta`	Hyperparameters for tree regularization prior. A terminal node of depth `d` will split with probability of `alpha * (1 + d)^(-beta)`. The default setting is `⁠(alpha, beta) = (0.95, 2)⁠` from Chipman et al. (2010).
`nu`, `q`	Values to calibrate hyperparameter of sigma prior. The default setting is `⁠(nu, q) = (3, 0.95)⁠` from Chipman et al. (2010).
`dir_alpha`	Hyperparameter of Dirichlet prior for selection probabilities. The default value is 5.
`parallel`	If `TRUE`, model fitting will be parallelized with respect to `N = nrow(X)`. Parallelization is recommended for very high `n` only. The default setting is FALSE.
`verbose`	If `TRUE`, message will be printed during training. If `FALSE`, message will be suppressed.

Details

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 $E[A|X]$ and the outcome model(s) $E[Y|A,X]$ 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).

Value

A bartcs object. A list object contains the following components.

mcmc_list

A mcmc.list object from coda package. mcmc_list contains the following items.

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 `num_post_sample * ncol(X)`.
`chains`	A list of results from each MCMC chain.
`model`	`separate` or `single`.
`label`	Column names of `X`.
`params`	Parameters used in the model.

References

Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266-298. doi:10.1214/09-AOAS285

Kim, C., Tec, M., & Zigler, C. M. (2023). Bayesian Nonparametric Adjustment of Confounding, Biometrics doi:10.1111/biom.13833

Examples

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
)
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
)

Count the number of OpenMP threads for parallel computation

Description

count_omp_thread() counts the number of OpenMP threads for parallel computation. If it returns 1, OpenMP is not viable.

Usage

count_omp_thread()
count_omp_thread()

Value

Number of OpenMP thread(s).

Examples

count_omp_thread()

count_omp_thread()

Infant Health and Development Program Data

Description

Infant Health and Development Program (IHDP) is a randomized experiment from 1985 to 1988 which studied the effect of home visits on cognitive test scores for infants.

Usage

ihdp
ihdp

Format

treatment: Given treatment.
y_factual: Observed outcome.
y_cfactual: Potential outcome given the opposite treatment.
mu0: Control conditional means.
mu1: Treated conditional means.
X1 ~ X6: Confounders with continuous values.
X7 ~ X25: Confounders with binary values.

Details

This dataset was first used by Hill (2011), then used by other researchers (Shalit et al. 2017, Louizos et al. 2017).

Source

Our version of dataset is the dataset used by Louizos et al. (2017). This is the first realization of 10 generated datasets and you can find other realizations from https://github.com/AMLab-Amsterdam/CEVAE.

References

Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217-240. doi:10.1198/jcgs.2010.08162

Louizos, C., Shalit, U., Mooij, J. M., Sontag, D., Zemel, R., & Welling, M. (2017). Causal effect inference with deep latent-variable models. Advances in neural information processing systems, 30. doi:10.48550/arXiv.1705.08821 https://github.com/AMLab-Amsterdam/CEVAE

Shalit, U., Johansson, F. D., & Sontag, D. (2017, July). Estimating individual treatment effect: generalization bounds and algorithms. In International Conference on Machine Learning (pp. 3076-3085). PMLR. doi:10.48550/arXiv.1606.03976

Draw plot for `bartcs` object

Description

Two options are available: posterior inclusion probability (PIP) plot and trace plot.

Usage

## S3 method for class 'bartcs'
plot(x, method = NULL, parameter = NULL, ...)
## S3 method for class 'bartcs'
plot(x, method = NULL, parameter = NULL, ...)

Arguments

`x`	A `bartcs` object.
`method`	"`pip`" for posterior inclusion probability plot or "`trace`" for trace plot.
`parameter`	Parameter for traceplot.
`...`	Additional arguments for PIP plot. Check `?ggcharts::bar_chart` for possible arguments.

Details

PIP plot

When a posterior sample is sampled during training, separate_bart() or single_bart() also counts which variables are included in the model and compute PIP for each variable. For bartcs object x, this is stored in x$var_count and x$var_prob respectively. plot(method = "pip") uses this information and draws plot using ggcharts::bar_chart().

Traceplot

Parameters are recorded for each MCMC iterations. Parameters include "ATE", "Y1", "Y0", "dir_alpha", and either "sigma2_out" from single_bart() or "sigma2_out1" and "sigma2_out0" from
separate_bart(). Vertical line indicates burn-in.

Value

A ggplot object of either PIP plot or trace plot.

Examples

data(ihdp, package = "bartcs")
x <- 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
)

# PIP plot
plot(x, method = "pip")
plot(x, method = "pip", top_n = 10)
plot(x, method = "pip", threshold = 0.5)
# Check `?ggcharts::bar_chart` for other possible arguments.

# trace plot
plot(x, method = "trace")
plot(x, method = "trace", "Y1")
plot(x, method = "trace", "dir_alpha")

data(ihdp, package = "bartcs")
x <- 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
)

# PIP plot
plot(x, method = "pip")
plot(x, method = "pip", top_n = 10)
plot(x, method = "pip", threshold = 0.5)
# Check `?ggcharts::bar_chart` for other possible arguments.

# trace plot
plot(x, method = "trace")
plot(x, method = "trace", "Y1")
plot(x, method = "trace", "dir_alpha")

Summary for `bartcs` object

Description

Provide summary for bartcs object.

Usage

## S3 method for class 'bartcs'
summary(object, ...)
## S3 method for class 'bartcs'
summary(object, ...)

Arguments

`object`	A `bartcs` object.
`...`	Additional arguments. Not yet supported.

Details

summary() provides 95% posterior credible interval for both aggregated outcome and individual outcomes from each MCMC chain.

Value

Provide list with the following components

`model`	`separate_bart` or `single_bart`.
`trt_value`	Treatment values for each treatment group: `trt_treated` for the treatment group and `trt_control` for the control group.
`tree_params`	Parameters for the tree structure.
`chain_params`	Parameters for MCMC chains.
`outcome`	Summary of outcomes from the model. This includes both aggregated outcome and individual outcomes from each MCMC chain.

Examples

data(ihdp, package = "bartcs")
x <- 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
)
summary(x)

data(ihdp, package = "bartcs")
x <- 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
)
summary(x)

Package 'bartcs'

Help Index

Fit BART models to select confounders and estimate treatment effect

Description

Usage

Arguments

Details

Value

References

Examples

Count the number of OpenMP threads for parallel computation

Description

Usage

Value

Examples

Infant Health and Development Program Data

Description

Usage

Format

Details

Source

References

Draw plot for bartcs object

Description

Usage

Arguments

Details

PIP plot

Traceplot

Value

Examples

Summary for bartcs object

Description

Usage

Arguments

Details

Value

Examples

Draw plot for `bartcs` object

Summary for `bartcs` object