Imputation ATT(g,t) estimator for bad controls

Usage

imputation_attgt(
  gt_data,
  xformula = ~1,
  bad_control_formula = NULL,
  d_covs_formula = NULL,
  bad_control_cov_formula = NULL,
  bad_control_d_cov_formula = NULL,
  bad_control_binary = FALSE,
  bad_control_identification_strategy = c("unconfoundedness", "did"),
  ...
)

Arguments

gt_data: data.frame from ptetools::two_by_two_subset
xformula: One-sided formula for general exogenous covariates, entered as their pre-treatment-period level (default ~1)
bad_control_formula: One-sided formula naming exactly one bad control variable (a time-varying covariate affected by treatment). NULL (the default) means no bad control at all.
d_covs_formula: One-sided formula for general exogenous covariates, entered as their change (post minus pre) rather than a level. Default NULL (unused).
bad_control_cov_formula: One-sided formula for auxiliary covariates (W in the paper) used to model the bad control's counterfactual evolution, entered as their pre-treatment-period level. Any variable name can be used here, including the outcome itself (to reproduce the old "lagged outcome as W" behavior). Default NULL (unused).
bad_control_d_cov_formula: Like bad_control_cov_formula, but entered as a change (post minus pre) rather than a level. Default NULL (unused).
bad_control_binary: Logical; whether the bad control is binary (detected automatically by didbc()). If TRUE, Step 1 (the bad-control evolution model) is fit by logistic regression instead of OLS, and the influence function is adjusted accordingly. The continuous case (FALSE, the default) is unaffected. Ignored when bad_control_identification_strategy = "did" (see below).
bad_control_identification_strategy: Which assumption identifies the bad control's untreated potential evolution: "unconfoundedness" (default), i.e. Covariate Unconfoundedness for the bad control given (bc_pre, W, Z), or "did", i.e. parallel trends for the bad control itself given (W, Z) (app:bad-control-parallel-trends in the supplementary appendix). "did" requires bad_control_formula to be non-NULL and does not distinguish a binary bad control.
...: unused

Details

This function only handles the shared leadin (pivoting gt_data to one row per unit, and constructing the covariate columns/names), then dispatches to imputation_unconfoundedness or imputation_did depending on bad_control_identification_strategy.