% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{summary.causalmixgpd_ps_fit}
\alias{summary.causalmixgpd_ps_fit}
\title{Summarize a propensity score fit}
\usage{
\method{summary}{causalmixgpd_ps_fit}(object, pars = NULL, probs = c(0.025, 0.5, 0.975), ...)
}
\arguments{
\item{object}{A \code{"causalmixgpd_ps_fit"} object.}

\item{pars}{Optional character vector of PS parameters to summarize. If
\code{NULL}, summarize all monitored parameters.}

\item{probs}{Numeric vector of posterior quantiles to report.}

\item{...}{Unused.}
}
\value{
An object of class \code{"summary.causalmixgpd_ps_fit"} with
elements \code{model} and \code{table}.
}
\description{
\code{summary.causalmixgpd_ps_fit()} returns posterior summaries for the
monitored PS-model parameters.
}
\details{
The summary is parameter based. For logit and probit models, it summarizes the
posterior draws of the coefficients that determine the latent linear
predictor, which is then mapped to \eqn{e(x)} by the chosen link function.
For the naive Bayes option, it summarizes the class-conditional parameters
used to factorize the treatment-assignment model.

This function does not compute fitted propensity scores for specific covariate
rows. It summarizes the posterior distribution of the PS model itself, which
is the nuisance model later used by causal prediction and treatment-effect
standardization.
}
