CRAN/E | did

did

Treatment Effects with Multiple Periods and Groups

Installation

About

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.

Citation did citation info
bcallaway11.github.io/did/
github.com/bcallaway11/did/

Key Metrics

Version 2.1.2
R ≥ 3.5
Published 2022-07-20 640 days ago
Needs compilation? no
License GPL-2
CRAN checks did results

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Maintainer

Maintainer

Brantly Callaway

brantly.callaway@uga.edu

Authors

Brantly Callaway

aut / cre

Pedro H. C. Sant'Anna

aut

Material

README
NEWS
Reference manual
Package source

In Views

CausalInference

Vignettes

Problems with two-way fixed-effects event-study regressions
Getting Started with the did Package
Writing Extensions to the did Package
Introduction to DiD with Multiple Time Periods
Pre-Testing in a DiD Setup using the did Package

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

did archive

Depends

R ≥ 3.5

Imports

BMisc ≥ 1.4.4
Matrix
pbapply
ggplot2
ggpubr
DRDID
generics
methods
tidyr

Suggests

rmarkdown
plm
here
knitr
covr

Reverse Imports

did2s