Estimate ATE via design-based approach — design_based_estimators

This function follows `design_based_estimators`, but runs the regression on the individual level data.

Usage

design_based_estimators_individual(
  formula,
  data = NULL,
  control_formula = NULL,
  weight = c("Person", "Cluster"),
  include_block_estimates = FALSE
)

Arguments

formula: Formula for outcome and treatment and nesting. If NULL, data is assumed to be in canonical form (see vignette for further discussion).
data: Data frame holding the outcome, treatment, and clustering/blocking columns referenced by `formula` (or already in canonical form if `formula` is NULL).
control_formula: What variables to control for, in the form of "~ X1 + X2".
weight: "Person" to weight the regression by individual (unweighted OLS), "Cluster" to weight so each cluster contributes equally (inverse cluster-size weights).
include_block_estimates: If TRUE, also return the block-by-block estimates used to build the overall estimate, in addition to the aggregate estimate.

Value

tibble of estimates using different varieties of the methods described in the paper.

Details

Degrees of freedom: unlike design_based_estimators(), this function works directly on individual-level data, so control_formula may mix level-1 (individual) and level-2 (cluster) covariates. The degrees of freedom adjustment here uses number_level2_controls(), which counts only the level-2 covariates – level-1 covariates do not cost the same degrees of freedom in this design-based framework (see the technical supplement's benchmark degrees of freedom of J - K - g - 1, where g is the number of level-2 covariates beyond the treatment indicator).