Nothing
#' @title Simulated data with known SACE
#'
#' @description This simulated dataset is to illustrate how to use \link[tbd]{sace} to estimate the SACE, and compare it with other naive methods. In this simulated data, by design, there is confounding between \code{Z} and \code{Y} caused by \code{X}, and confounding between \code{S} and \code{Y} caused by \code{X}.
#'
#' @docType data
#' @keywords datasets
#' @name simulated_data
#' @format A data frame with 5000 observations and 7 variables. \code{Z}, \code{A}, \code{Y}, \code{S} are 1-dimensional, and \code{X} is 3-dimensional. The variables are as follows:
#' \describe{
#' \item{Z}{Binary treatment}
#' \item{X.X1}{A factor covariate with 2 levels (1 and -1)}
#' \item{X.V2}{A continuous covariate}
#' \item{X.V3}{A contunuous covariate}
#' \item{A}{The substitution variable which is continuous}
#' \item{Y}{The continuous outcome. \code{NA} where \eqn{S = 0}}
#' \item{S}{The survival indicator. \code{1} means survival and \code{0} means death.}
#' }
#' @source The dataset is generated by the simulation design of \cite{Wang et al. 2017} with \eqn{\delta_1 = 1} and \eqn{\delta_0 = 1}, which allows confounding between \code{Z} and \code{Y} caused by \code{X}, and confounding between \code{S} and \code{Y} caused by \code{X}.
#' @references Linbo Wang, Xiao-Hua Zhou, Thomas S. Richardson; Identification and estimation of causal effects with outcomes truncated by death, Biometrika, Volume 104, Issue 3, 1 September 2017, Pages 597-612, \url{https://doi.org/10.1093/biomet/asx034}
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.