% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/si.R
\name{si_inference}
\alias{si_inference}
\title{Non-parametric Inference for SI (Agarwal et al. 2025)}
\usage{
si_inference(
  fit,
  method = c("bootstrap", "jackknife", "jackknife_global"),
  n_boot = 499L,
  level = 0.95,
  alternative = c("two.sided", "greater", "less"),
  seed = NULL
)
}
\arguments{
\item{fit}{A \code{coresynth} object from \code{\link[=scm_fit]{scm_fit()}} with \code{method = "si"}.}

\item{method}{\code{"bootstrap"} (default) or \code{"jackknife"}.}

\item{n_boot}{Number of bootstrap replications (default 499L; ignored for jackknife).}

\item{level}{Confidence level (default 0.95).}

\item{alternative}{\code{"two.sided"} (default), \code{"greater"}, or \code{"less"}.}

\item{seed}{RNG seed for reproducibility (default NULL).}
}
\value{
A list of class \code{coresynth_inference}.
}
\description{
Estimates SE and confidence intervals for the ATT via non-parametric cluster
bootstrap or jackknife over control units. Works for both sharp and staggered
SI fits. For staggered fits, bootstrap resamples each cohort's control pool
independently, and jackknife uses a per-cohort LOO with delta-method variance
aggregation.
}
