% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scm.R
\name{augment_scm}
\alias{augment_scm}
\title{Augmented Synthetic Control Method (Ridge ASCM)}
\usage{
augment_scm(fit, lambda_ridge = NULL)
}
\arguments{
\item{fit}{A \code{coresynth} object from \code{\link[=scm_fit]{scm_fit()}} with \code{method = "scm"}.}

\item{lambda_ridge}{Ridge penalty (non-negative). \code{NULL} (default) selects
the penalty by leave-one-out cross-validation on the control units.}
}
\value{
A list with:
\itemize{
\item \code{att_aug}: Augmented ATT estimate
\item \code{delta}: Bias correction term (m_tr_post - sum_j W_j m_j_post)
\item \code{att_scm}: Original SCM ATT for comparison
\item \code{lambda_ridge}: Ridge penalty used
\item \code{beta_hat}: Ridge regression coefficients (length T_pre)
}
}
\description{
Applies a ridge-regression-based bias correction to a fitted SCM object,
following Ben-Michael, Feller & Rothstein (2021, JASA). The corrected
estimator is:
}
\details{
tau_aug = tau_SCM + (m_tr_post - sum_j W_j * m_j_post)

where m_i_post = Y_pre_i' beta_hat is the ridge outcome model prediction
for unit i's mean post-treatment outcome, and beta_hat is estimated by
ridge regression across control units.
}
