Treatment Effects with Multiple Periods and Groups
Aggregate Group-Time Average Treatment Effects
AGGTEobj
Group-Time Average Treatment Effects
build_sim_dataset
citation
Compute Aggregated Treatment Effect Parameters
Compute Group-Time Average Treatment Effects
Compute Group-Time Average Treatment Effects
Pre-Test of Conditional Parallel Trends Assumption
Difference in Differences
DIDparams
Get an influence function for particular aggregate parameters
Take influence function and return standard errors
Plot AGGTEobj objects
Plot MP objects using ggplot2
Plot did objects using ggplot2
glance model characteristics from AGGTEobj objects
glance model characteristics from MP objects
gplot
indicator
Multiplier Bootstrap
MP
MP.TEST
Process did Function Arguments
Process did Function Arguments
print.AGGTEobj
print.MP
Process Results from compute.att_gt()
tidy results
reset.sim
Run ATT estimation for a given group-time pair
sim
splot
Summary Aggregate Treatment Effect Parameter Objects
summary.MP
summary.MP.TEST
Multiplier Bootstrap for Conditional Moment Test
tidy results from AGGTEobj objects
tidy results from MP objects
trimmer
Compute extra term in influence function due to estimating weights
The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group. Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time. This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>. The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time. These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these. There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.