Lin estimator on LASSO-selected covariates

Runs lasso_select_covariates and fits lm_lin on the selected set: a separate covariate slope in each treatment arm, with covariates centered at their full-sample means so the treatment coefficient remains an estimate of the average treatment effect.

Usage

lm_lin_lasso(
  formula,
  covariates,
  data,
  weights = NULL,
  subset = NULL,
  clusters = NULL,
  se_type = NULL,
  ci = TRUE,
  alpha = 0.05,
  return_vcov = TRUE,
  try_cholesky = FALSE,
  lasso_args = list()
)

Arguments

formula

A two-sided formula outcome ~ treatment.

covariates

Candidate covariates: a one-sided formula, a character vector of column names, or a bare expression.

data

A data frame.

weights, subset, clusters, se_type, ci, alpha, return_vcov, try_cholesky

Passed through to lm_lin or lm_robust.

lasso_args

A named list of further arguments for lasso_select_covariates, such as lambda_rule or seed.

Value

An lm_robust object, carrying the attributes described in adjustment.

Fallback

lm_lin cannot be fitted with no covariates, and an adjusted fit can come back degenerate when the selected set is nearly collinear within an arm. In either case this function returns an unadjusted lm_robust fit, which for a binary treatment is the difference in means. Because that substitution changes the specification, it is recorded rather than hidden: read it back with adjustment and selected_covariates. The specific triggers are that selection returned ~1, that lm_lin threw, or that the treatment row of the adjusted fit has a non-finite standard error.

See also

lm_robust_lasso for additive adjustment, lm_moderator_lasso for a treatment-by-moderator model.

Other adjusted estimators: lm_moderator_lasso(), lm_robust_lasso()

Examples

set.seed(1)
n <- 400
dat <- data.frame(Z = rep(0:1, n / 2), X1 = rnorm(n), X2 = rnorm(n))
dat$Y <- 0.5 * dat$Z + 1.5 * dat$X1 + rnorm(n)

fit <- lm_lin_lasso(Y ~ Z, ~ X1 + X2, data = dat)
adjustment(fit)
#> [1] "lin"
selected_covariates(fit)
#> [1] "X1"