Estimate the Average Treatment Effect on the Treated (ATT)

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

Usage

morie_estimate_att(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: att, se, ci_lower, ci_upper, n_treated.

Details

Propensity-score estimation delegates to WeightIt when installed (via morie_estimate_propensity_scores); the weighted-difference and influence-function SE run inline.

Examples

set.seed(2)
df <- data.frame(t = rbinom(200, 1, 0.4), y = rnorm(200), x = rnorm(200))
morie_estimate_att(df, "t", "y", "x")
#> $att
#> [1] 0.01887334
#> 
#> $se
#> [1] 0.1568311
#> 
#> $ci_lower
#> [1] -0.2885156
#> 
#> $ci_upper
#> [1] 0.3262622
#> 
#> $n_treated
#> [1] 75
#>