Estimate the Average Treatment Effect on the Controls (ATC)

Control units receive weight 1; treated units receive \(w_i = (1-\hat{e}(X_i))/\hat{e}(X_i)\).

Usage

morie_estimate_atc(data, treatment, outcome, covariates, propensity_col = NULL)

Arguments

data: A data frame.
treatment: Name of the binary treatment column.
outcome: Name of the outcome column.
covariates: Character vector of covariate names.
propensity_col: Optional: name of a pre-computed propensity score column.

Value

Named list: atc, se, ci_lower, ci_upper, n_control.

Details

The propensity scores come from morie_estimate_propensity_scores() (native logistic regression); the weighted difference and its influence-function SE are closed form.

Examples

if (requireNamespace("AIPW", quietly = TRUE)) {
  set.seed(1)
  df <- data.frame(t = rbinom(200, 1, 0.4), y = rnorm(200), x = rnorm(200))
  morie_estimate_atc(df, "t", "y", "x")
}
#> $atc
#> [1] -0.04353784
#> 
#> $se
#> [1] 0.1409701
#> 
#> $ci_lower
#> [1] -0.3198393
#> 
#> $ci_upper
#> [1] 0.2327636
#> 
#> $n_control
#> [1] 117
#>