% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-datasets.R
\name{sim_causal_qte}
\alias{sim_causal_qte}
\title{Simulate causal quantile-treatment-effect data}
\usage{
sim_causal_qte(n = 300, seed = NULL)
}
\arguments{
\item{n}{Integer sample size.}

\item{seed}{Optional random seed.}
}
\value{
List with components \code{y}, \code{t}, and \code{X}; \code{A} is included as a backward-compatible alias
for \code{t}.
}
\description{
Generate a treatment indicator, covariates, and a continuous outcome with both location and
tail heterogeneity. The resulting structure is intended for examples involving
\code{\link[=dpmix.causal]{dpmix.causal()}}, \code{\link[=dpmgpd.causal]{dpmgpd.causal()}}, \code{\link[=qte]{qte()}}, and \code{\link[=cqte]{cqte()}}.
}
\details{
Treatment assignment is generated from a logistic propensity score
\deqn{
\Pr(T = 1 \mid X) = \operatorname{logit}^{-1}(\eta(X)),
}
and the observed outcome combines baseline covariate effects, an average treatment shift, and
a covariate-dependent tail amplification for treated units. This produces data where marginal
and conditional quantile effects differ across the outcome distribution.

The returned list can be converted directly into the arguments expected by the causal fitting
wrappers after minor formatting.
}
\seealso{
\code{\link[=sim_bulk_tail]{sim_bulk_tail()}}, \code{\link[=dpmgpd.causal]{dpmgpd.causal()}}, \code{\link[=qte]{qte()}}, \code{\link[=cqte]{cqte()}}.

Other simulation helpers: 
\code{\link{sim_bulk_tail}()},
\code{\link{sim_survival_tail}()}
}
\concept{simulation helpers}
