% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{print.causalmixgpd_ate}
\alias{print.causalmixgpd_ate}
\title{Print an ATE-style effect object}
\usage{
\method{print}{causalmixgpd_ate}(x, digits = 3, max_rows = 6, ...)
}
\arguments{
\item{x}{A \code{"causalmixgpd_ate"} object from \code{ate()}.}

\item{digits}{Number of digits to display.}

\item{max_rows}{Maximum number of estimate rows to display.}

\item{...}{Unused.}
}
\value{
The object \code{x}, invisibly.
}
\description{
\code{print.causalmixgpd_ate()} prints a compact summary for objects
produced by \code{\link{ate}}, \code{\link{att}}, \code{\link{cate}}, or
\code{\link{ate_rmean}}.
}
\details{
These objects summarize posterior treatment contrasts on the mean scale. For
the marginal average treatment effect,
\deqn{\Delta = E(Y^1) - E(Y^0).}
\code{att()} changes the standardization target to the treated population,
\code{cate()} conditions on supplied covariate profiles, and \code{ate_rmean()} replaces
the ordinary mean by a restricted mean
\eqn{\int_0^c S_a(t)\,dt} up to the chosen truncation point.

The print method shows the main effect table and setup metadata, but it is not
a full diagnostic report. Use \code{summary()} for tabular summaries and \code{plot()}
for graphical inspection of the same treatment-effect object.
}
\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
mcmc_small <- list(niter = 100, nburnin = 50, thin = 1, nchains = 1, seed = 1)
cb <- build_causal_bundle(y = y, X = X, A = A, backend = "sb", kernel = "normal",
                         components = 3, mcmc_outcome = mcmc_small, mcmc_ps = mcmc_small)
fit <- run_mcmc_causal(cb, show_progress = FALSE)
a <- ate(fit, interval = "credible")
print(a)
}
}
\seealso{
\code{\link{summary.causalmixgpd_ate}},
\code{\link{plot.causalmixgpd_ate}}, \code{\link{ate}},
\code{\link{cate}}.
}
