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

\item{...}{Unused.}
}
\value{
An object of class \code{"summary.causalmixgpd_qte"} with
\code{overall}, \code{quantile_summary}, \code{effect_table},
\code{ci_summary}, \code{meta}, and the original \code{object}.
}
\description{
\code{summary.causalmixgpd_qte()} converts QTE, QTT, or CQTE output into a
tabular summary suitable for reporting.
}
\details{
The summary reorganizes the posterior effect object into reporting tables. The
target estimand remains a quantile contrast,
\deqn{\Delta(\tau) = Q_{Y^1}(\tau) - Q_{Y^0}(\tau),}
with the appropriate marginal, treated-standardized, or conditional
interpretation depending on whether the source object came from \code{qte()},
\code{qtt()}, or \code{cqte()}.

Besides the effect table itself, the summary records the quantile grid, the
interval settings, and per-quantile distributional summaries when posterior
draws are available. This makes the object convenient for reporting and
downstream printing without recomputing the estimand.
}
\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)
q <- qte(fit, probs = c(0.25, 0.5, 0.75), interval = "credible")
summary(q)
}
}
\seealso{
\code{\link{print.causalmixgpd_qte}},
\code{\link{plot.causalmixgpd_qte}}, \code{\link{qte}},
\code{\link{cqte}}.
}
