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

\item{...}{Unused.}
}
\value{
An object of class \code{"summary.causalmixgpd_ate"} with
\code{overall}, \code{ate_stats}, \code{effect_table},
\code{ci_summary}, \code{meta}, and the original \code{object}.
}
\description{
\code{summary.causalmixgpd_ate()} converts ATE, ATT, CATE, or restricted-mean
output into a tabular summary suitable for reporting.
}
\details{
The summary reorganizes posterior treatment-effect output on the mean scale.
Depending on the source object, the target estimand is a marginal ATE,
treated-standardized ATT, conditional ATE, or a restricted-mean contrast
based on
\deqn{\int_0^c \{S_1(t) - S_0(t)\}\,dt.}

The returned object stores overall metadata, effect tables, and interval
summaries in a reporting-friendly format. It does not refit the model or
recompute arm-specific predictions.
}
\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")
summary(a)
}
}
\seealso{
\code{\link{print.causalmixgpd_ate}},
\code{\link{plot.causalmixgpd_ate}}, \code{\link{ate}},
\code{\link{cate}}.
}
