% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{print.causalmixgpd_causal_bundle}
\alias{print.causalmixgpd_causal_bundle}
\title{Print a causal workflow bundle}
\usage{
\method{print}{causalmixgpd_causal_bundle}(x, code = FALSE, max_code_lines = 200L, ...)
}
\arguments{
\item{x}{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{print.causalmixgpd_causal_bundle()} gives a compact structural summary
of the pre-run causal bundle created by \code{\link{build_causal_bundle}}.
}
\details{
A causal bundle collects three pre-MCMC building blocks: the optional
propensity-score model for \eqn{e(x) = \Pr(A = 1 \mid X = x)}, the control
outcome model for \eqn{Y^0}, and the treated outcome model for \eqn{Y^1}. The
printed output aligns those blocks side by side so the user can verify that
the treated and control outcome specifications are coherent before sampling.

No causal estimand is computed at this stage. The bundle only records the
structural assumptions that will later support estimands such as
\eqn{E(Y^1 - Y^0 \mid X = x)} or
\eqn{Q_{Y^1}(\tau \mid X = x) - Q_{Y^0}(\tau \mid X = x)}.
}
\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")
print(cb)
}
}
\seealso{
\code{\link{summary.causalmixgpd_causal_bundle}},
\code{\link{run_mcmc_causal}}, \code{\link{ate}}, \code{\link{qte}}.
}
