Covid ATT(g,t) Estimator — covid

Computes a group-time average treatment effect and influence function using an unconfoundedness-type identification strategy. This estimator is appropriate when parallel trends is implausible but a selection-on-observables assumption holds in levels (rather than differences) — e.g., during the early COVID-19 pandemic.

Originally from Callaway and Li (2021). Moved into ptetools from the ppe package.

Usage

covid_attgt(gt_data, xformla, d_outcome = FALSE, d_covs_formula = ~-1, ...)

Arguments

gt_data: data that is "local" to a particular group-time average treatment effect, structured as a gt_data_frame
xformla: one-sided formula for covariates used in the propensity score and outcome regression models
d_outcome: logical; if TRUE, use first-differenced outcomes. Default is FALSE (levels).
d_covs_formula: one-sided formula for covariates to include as changes (differences). Default is ~-1 (no change covariates).
...: extra arguments; not used

Value

attgt_if object

References

Callaway, B. and Li, T. (2021). Policy Evaluation during a Pandemic. https://arxiv.org/abs/2105.06927