G-computation ATE with bootstrap SE — estimate_ate

Thin wrapper over stdReg::stdGlm() (Sjolander's regression-standardisation back end) when stdReg is installed. Without stdReg, falls back to an inline outcome- regression + bootstrap implementation (500 resamples, seed 42) that mirrors the legacy rmorie behaviour.

Usage

estimate_ate_gcomputation(
  data,
  treatment,
  outcome,
  covariates,
  outcome_model = "linear"
)

Arguments

data: Data frame with all required columns.
treatment: Binary treatment column (0/1).
outcome: Outcome column.
covariates: Character vector of covariates.
outcome_model: "linear" (OLS) or "logistic" (logit GLM).

Value

Named list with ate, se, ci_lower, ci_upper, n_obs, outcome_model.

Examples

set.seed(1)
n <- 300
X <- matrix(rnorm(n * 3), n, 3)
tr <- rbinom(n, 1, plogis(X[, 1]))
y <- 2.5 * tr + drop(X %*% c(1, 0.5, -0.7)) + rnorm(n)
d <- data.frame(y = y, d = tr, x1 = X[, 1], x2 = X[, 2], x3 = X[, 3])
res <- estimate_ate_gcomputation(d, treatment = "d", outcome = "y",
                                 covariates = c("x1", "x2", "x3"))
res$ate
#> [1] 2.41178