
Imputation estimator under Covariate Unconfoundedness for the bad control
Source:R/imputation.R
imputation_unconfoundedness.RdImplements the two-step imputation estimator and its influence function
(eq:psi-ra-final in the supplementary appendix) under Covariate
Unconfoundedness: the bad control's untreated potential evolution is
modeled from (bc_pre, W, Z) among the comparison group. Called by
imputation_attgt when
bad_control_identification_strategy = "unconfoundedness".
Usage
imputation_unconfoundedness(
wide_data,
pre_data,
post_data,
D,
n,
x_names,
dx_names,
bc_cov_names,
bc_dcov_names,
comparison_idx,
bad_control_formula,
bad_control_binary
)Arguments
- wide_data
one row per unit, as constructed by
imputation_attgt- pre_data, post_data
the pre/post-period long-format subsets of the original
gt_data, as constructed byimputation_attgt- D
treatment indicator vector, aligned to
wide_data- n
number of units (
nrow(wide_data))- x_names, dx_names
names of the general exogenous covariate columns (level and change, respectively) already merged into
wide_data- bc_cov_names, bc_dcov_names
names of the bad-control auxiliary covariate columns (level and change, respectively, W in the paper) already merged into
wide_data- comparison_idx
row indices of
wide_datain the comparison (untreated) group- bad_control_formula
One-sided formula naming the bad control variable, or
NULLfor no bad control at all- bad_control_binary
Logical; whether the bad control is binary (see
imputation_attgt)