% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{survey_obs}
\alias{survey_obs}
\title{Simulated Survey Observational Data}
\format{
A data frame with approximately 120 rows and 9 variables:
\describe{
  \item{Y}{Observed outcome (continuous).}
  \item{Z}{Binary treatment indicator (1 = treated, 0 = control).}
  \item{X1, X2, X3, X4, X5, X6}{Pre-treatment covariates (continuous).
    The true propensity and prognostic models include an \code{X1:X2}
    interaction.}
  \item{survey_weight}{Survey design weight (inverse selection probability).}
}
}
\source{
Simulated data; see \code{data-raw/make_survey_data.R}.
}
\usage{
survey_obs
}
\description{
A simulated dataset drawn from a survey-weighted observational study with
treatment-dependent (retrospective) sampling. Contains 6 covariates, a
binary treatment indicator, observed outcome, and survey design weights.
The true PATE is approximately 0.8 and the true PATT approximately 1.0.
}
\details{
Generated by a simulation where:
\itemize{
  \item Treatment assignment: \eqn{P(Z=1|X) = \text{logit}^{-1}(0.3 + 0.6 X_1 + 0.4 X_2 - 0.3 X_3 + 0.2 X_1 X_2)}.
  \item Outcome model: \eqn{Y(0) = 1 + X_1 + 0.5 X_2 - 0.3 X_3 + 0.2 X_4 + 0.3 X_1 X_2 + \varepsilon},
    with treatment effect \eqn{\tau(X) = 0.8 + 0.2 X_1}.
  \item Survey selection: treatment-dependent (retrospective) with
    \eqn{P(S=1|Z,X) = \text{logit}^{-1}(-2 + 0.3 Z + 0.2 X_1 + 0.15 X_2)}.
}
}
\examples{
data(survey_obs)
head(survey_obs)

# Estimate PATE
fit <- wdsmatchATE(Y = survey_obs$Y, X = survey_obs[, 3:8],
                   Z = survey_obs$Z, weights = survey_obs$survey_weight,
                   M = 3, varest = FALSE)
fit

}
\keyword{datasets}
