Skip to contents

Variance Estimation for Mixed-Subjects Design

bootstrap_variance.Rd

Variance estimation methods including fold-respecting bootstrap. Bootstrap Variance Estimation

Computes bootstrap variance estimates for MSD estimators using a fold-respecting resampling procedure.

Usage

bootstrap_variance(
  data,
  estimator = c("dim", "greg", "ppi", "dt", "dip", "dip_pp", "dt_dip"),
  n_bootstrap = 1000,
  n_folds = 2,
  conf_level = 0.95,
  seed = NULL
)

Arguments

data

An msd_data object created by msd_data

estimator

Character string specifying the estimator. One of: "dim", "greg", "ppi", "dt", "dip", "dip_pp", "dt_dip"

n_bootstrap

Number of bootstrap replications (default 1000)

n_folds

Number of folds for cross-fitting (default 2, used for tuned estimators)

conf_level

Confidence level for confidence intervals (default 0.95)

seed

Random seed for reproducibility (optional)

Value

A list containing:

estimate

Point estimate from the original data

variance

Bootstrap variance estimate

se

Bootstrap standard error

ci_lower, ci_upper

Bootstrap percentile confidence interval

bootstrap_estimates

Vector of bootstrap estimates

Details

The fold-respecting bootstrap resamples within each stratum:

  1. Resample observed treatment units with replacement

  2. Resample observed control units with replacement

  3. Resample unlabeled units with replacement

  4. Recompute the estimator on the bootstrap sample

For cross-fit estimators, the fold assignments are regenerated for each bootstrap replicate to properly account for the cross-fitting variance.

Examples

if (FALSE) { # \dontrun{
# Create sample data
obs_df <- data.frame(
  Y = rnorm(100),
  S0 = rnorm(100),
  S1 = rnorm(100),
  D = rep(c(1, 0), each = 50)
)
unobs_df <- data.frame(
  S0 = rnorm(200),
  S1 = rnorm(200),
  D = rep(c(1, 0), each = 100)
)
msd <- msd_data(observed = obs_df, unobserved = unobs_df)

# Bootstrap variance for D-T DiP
boot_result <- bootstrap_variance(msd, "dt_dip", n_bootstrap = 500)
print(boot_result)
} # }