% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{summary.causalmixgpd_causal_bundle}
\alias{summary.causalmixgpd_causal_bundle}
\title{Summarize a causal workflow bundle}
\usage{
\method{summary}{causalmixgpd_causal_bundle}(object, code = FALSE, max_code_lines = 200L, ...)
}
\arguments{
\item{object}{A \code{"causalmixgpd_causal_bundle"} object.}

\item{code}{Logical; if TRUE, print generated NIMBLE code for each block.}

\item{max_code_lines}{Integer; maximum number of code lines to print when \code{code=TRUE}.}

\item{...}{Unused.}
}
\value{
The input object (invisibly).
}
\description{
\code{summary.causalmixgpd_causal_bundle()} is the bundle-level validation
checkpoint for the causal workflow.
}
\details{
This summary is meant to be read before posterior sampling. It reports the
stored propensity-score specification, the treated and control outcome model
definitions, and the sample split across treatment arms. In other words, it
verifies the model ingredients for the causal decomposition
\eqn{(e(x), f_0(y \mid x), f_1(y \mid x))}.

Since no MCMC has been run yet, the summary contains only structural
information. Posterior treatment-effect summaries become available after
\code{\link{run_mcmc_causal}} through functions such as \code{\link{ate}} and
\code{\link{qte}}.
}
\examples{
\donttest{
N <- 25
X <- data.frame(x1 = stats::rnorm(N))
A <- stats::rbinom(N, 1, 0.5)
y <- abs(stats::rnorm(N)) + 0.1
cb <- build_causal_bundle(y = y, X = X, A = A, backend = "sb", kernel = "normal")
summary(cb)
}
}
\seealso{
\code{\link{print.causalmixgpd_causal_bundle}},
\code{\link{run_mcmc_causal}}.
}
