## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(AugBalWeight)

## ----lalonde_example----------------------------------------------------------
# Load canonical LaLonde dataset
data(lalonde_data)

# Specify covariates
covariates <- c("age", "educ", "black", "hisp", "married", "re74", "re75", "age2", "educ2", "re742")
X <- as.matrix(lalonde_data[, covariates])
Y <- lalonde_data$re78
Z <- lalonde_data$treat

# Estimate ATT using Ridge-augmented L2 balancing weights
fit_att <- aug_bal_att(
  Y = Y,
  Z = Z,
  X = X,
  type = "l2",
  outcome_model = "ridge",
  tuning_method = "cv_outcome"
)

# Print ATT estimate and confidence interval
print(fit_att)

## ----summary_example----------------------------------------------------------
summary(fit_att)

## ----plot_example, fig.width = 7, fig.height = 5------------------------------
# Plot covariate balance diagnostic
plot(fit_att, which = 1)

# Plot distribution of estimated balancing weights
plot(fit_att, which = 2)

## ----double_lasso_example-----------------------------------------------------
fit_lasso <- double_lasso(
  Y = Y[Z == 0],
  X_p = X[Z == 0, ],
  target_mean = colMeans(X[Z == 1, ]),
  lambda = 0.05,
  delta = 0.05
)

cat("Active outcome features :", fit_lasso$active_outcome, "\n")
cat("Active balance features :", fit_lasso$active_balance, "\n")
cat("Active union features   :", fit_lasso$active_union, "\n")