Last updated: 01 August 2021

Notes

This repository tracks the recent developments in the Difference-in-Difference (DiD) literature. Currently, it is just a dump of my bookmarks from different websites including Twitter, GitHub, YouTube etc. This will be sorted out over time as the literature converges to some consensus. But this might still take a while.

This is a working document, if you want to contribute, just message, or open an issue on GitHub.

Why do DiD?

TO BE ADDED

Stata packages

Name	Installation	Package by	Reference paper
bacondecomp	ssc install bacondecomp, replace	Andrew Goodman-Bacon Thomas Goldring Austin Nichols	Andrew Goodman-Bacon (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics
eventstudyinteract	See end of table	Liyang Sun	Liyang Sun, Sarah Abraham (2020). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics.
did_multiplegt	ssc install did_multiplegt, replace	Clément de Chaisemartin Xavier D'Haultfoeuille	Clément de Chaisemartin, Xavier D'Haultfoeuille (2020). Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects. American Economic Review. Clément de Chaisemartin, Xavier D'Haultfoeuille (2021). Two-way fixed effects regressions with several treatments. Clément de Chaisemartin, Xavier D'Haultfoeuille (2021). Difference-in-Differences Estimators of Inter-temporal Treatment Effects.
did_imputation	ssc install did_imputation, replace	Kirill Borusyak Xavier Jaravel Jann Spiess	Kirill Borusyak , Xavier Jaravel , Jann Spiess (2021). Revisiting Event Study Designs: Robust and Efficient Estimation.
drdid	See end of table	Fernando Rios-Avila Asjad Naqvi Pedro H.C. Sant'Anna	Pedro H.C. Sant'Anna, Jun Zhao (2020). Doubly robust difference-in-differences estimators, Journal of Econometrics.
csdid	See end of table	Fernando Rios-Avila	Brantly Callaway, Pedro H.C. Sant'Anna (2020). Difference-in-Differences with multiple time periods, Journal of Econometrics.
flexpaneldid	ssc install flexpaneldid, replace	Eva Dettmann Alexander Giebler Antje Weyh	Eva Dettmann, Alexander Giebler, Antje Weyh (2020). Flexpaneldid: A Stata Toolbox for Causal Analysis with Varying Treatment Time and Duration. IWH Discussion Papers No. 3/2020
xtevent	See end of table	Simon Freyaldenhoven Christian Hansen Jorge Perez Perez Jesse M. Shapiro	Simon Freyaldenhoven, Christian Hansen, Jesse M. Shapiro (2019). Pre-event Trends in the Panel Event-Study Design. American Economic Review.
did2s	ssc install did2s, replace	Kyle Butts	John Gardner (2021). Two-stage differences in differences.
stackedev	See end of table	Joshua Bleiberg	Doruk Cengiz , Arindrajit Dube , Attila Lindner, Ben Zipperer (2019). The effect of minimum wages on low-wage jobs. The Quarterly Journal of Economics.

Note: The length of the installation paths from GitHub repositories is messing up the table. Till this is sorted out, links are here:

• bacondecomp alternative: net install ddtiming, from(https://tgoldring.com/code/)
• drdid: net install drdid, from ("https://raw.githubusercontent.com/friosavila/csdid_drdid/v0.1/code") replace
• csdid: net install csdid, from ("https://raw.githubusercontent.com/friosavila/csdid_drdid/main/code/") replace
• eventstudyinteract : net install eventstudyinteract, from("https://raw.githubusercontent.com/lsun20/EventStudyInteract/main/") replace
• xtevent : Manually download and install from https://simonfreyaldenhoven.github.io/software/
• stackedev: net install stackedev, from("https://raw.githubusercontent.com/joshbleiberg/stackedev/main/")

How to use these packages?

For individual packages, check their helpfiles for example code.

For using and plotting multiple DiD packages in Stata, the event_plot command (ssc install event_plot, replace) by Kirill Borusyak is highly recommended. It estimates and combines results from five different estimators. Example of how to do event study plots using different packages is given in the five_estimators_example.do dofile on GitHub.

The event_plot usage example has been extended twice:

• David Burgherr has a dofile on Dropbox.

• Pietro Santoleri has a dofile on GitHub that plots seven different estimators.

R packages

Packages installation paths have been split across lines to preverse table formatting in Markdown.

Name	Installation	Package by	Reference paper
bacondecomp	install.packages ("bacondecomp")	Evan Flack Ed Jee	Andrew Goodman-Bacon (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics.
fixest with sunab() function	install.packages ("fixest")	Laurent Bergé	Liyang Sun, Sarah Abraham (2020). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics.
TwoWayFEWeights	install.packages ("TwoWayFEWeights")	Shuo Zhang	Clément de Chaisemartin, Xavier D'Haultfoeuille (2021). Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects.
DIDmultiplegt [not as recent as Stata version]	install.packages ("DIDmultiplegt")	Shuo Zhang	Clément de Chaisemartin, Xavier D'Haultfoeuille (2020). Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects. American Economic Review.
drdid	devtools::install_github ("pedrohcgs/DRDID")	Pedro H.C. Sant'Anna Jun Zhao	Pedro H.C. Sant'Anna , Jun Zhao (2020). Doubly robust difference-in-differences estimators, Journal of Econometrics.
did	install.packages("did")	Pedro H.C. Sant'Anna Brantly Callaway	Brantly Callaway, Pedro H.C. Sant'Anna (2020). Difference-in-Differences with multiple time periods, Journal of Econometrics.
staggered	devtools::install_github ("jonathandroth/staggered")	Jonathan Roth Pedro H.C. Sant'Anna	Jonathan Roth , Pedro H.C. Sant'Anna (2021). Efficient Estimation for Staggered Rollout Designs
didimputation	devtools::install_github ("kylebutts/didimputation")	Kyle Butts	Kirill Borusyak , Xavier Jaravel , Jann Spiess (2021). Revisiting Event Study Designs: Robust and Efficient Estimation.
did2s	devtools::install_github ("kylebutts/did2s")	Kyle Butts	John Gardner (2021). Two-stage differences in differences. Kirill Borusyak , Xavier Jaravel , Jann Spiess (2021). Revisiting Event Study Designs: Robust and Efficient Estimation Liyang Sun, Sarah Abraham (2020). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics. Brantly Callaway, Pedro H.C. Sant'Anna (2020). Difference-in-Differences with multiple time periods, Journal of Econometrics. Jonathan Roth , Pedro H.C. Sant'Anna (2021). Efficient Estimation for Staggered Rollout Designs.

Julia packages

This is still being updated. See links below for details. There is also a Julia DiffinDiff group on GitHub which contains various packages.

Name	Installation	Package by	Reference paper
InteractionWeightedDIDs.jl	GitHub
SynthControl.jl	GitHub

Literature

Papers

Papers are in alphabetical order by last name. Papers without journals are pre-prints. Please click on paper links for details. SORTABLE TABLE TO BE ADDED.

Dmitry Arkhangelsky , Guido Imbens, Lihua Lei , Xiaoman Luo (2021). Double-Robust Two-Way-Fixed-Effects Regression For Panel Data.

Kirill Borusyak , Xavier Jaravel , Jann Spiess (2021). Revisiting Event Study Designs: Robust and Efficient Estimation.

Brantly Callaway, Andrew Goodman-Bacon, Pedro H.C. Sant'Anna. Difference-in-Differences with a Continuous Treatment.

Brantly Callaway, Pedro H.C. Sant'Anna (2020). Difference-in-Differences with multiple time periods, Journal of Econometrics.

Clément de Chaisemartin, Xavier D'Haultfoeuille (2020). Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects. American Economic Review.

Clément de Chaisemartin, Xavier D'Haultfoeuille (2021). Two-way fixed effects regressions with several treatments.

Clément de Chaisemartin, Xavier D'Haultfoeuille (2021). Difference-in-Differences Estimators of Inter-temporal Treatment Effects.

Simon Freyaldenhoven, Christian Hansen, Jesse M. Shapiro (2019). Pre-event Trends in the Panel Event-Study Design. American Economic Review.

John Gardner (2021). Two-stage differences in differences.

Andrew Goodman-Bacon (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics.

Jonathan Roth , Pedro H.C. Sant'Anna (2021). Efficient Estimation for Staggered Rollout Designs.

Pedro H.C. Sant'Anna, Jun Zhao (2020). Doubly robust difference-in-differences estimators, Journal of Econometrics.

Liyang Sun, Sarah Abraham (2020). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics.

Books

Scott Cunningham (2020). Causal Inference: The Mix Tape.

Nick Huntington-Klein (2021). The Effect.

DiD knowledge curation

Here are people who are actively involved in curating information on the latest DiD developments. This includes blogs, lecture series, tweets.

Events and Videos

Scott Cunningham : CodeChella the ultimate DiD event Workshop 1: Friday July 16th, 2021 and Workshop 2: Friday July 23, 2021 which will be live on Twitch. The videos from the first workshop are now up on YouTube.

Chloe East organizes an online DiD reading group.

Taylor J. Wright organizes an online DiD reading group. The lecture recordings can also be viewed on YouTube.

Blogs

Scott Cunningham : Scott's Substack is the goto place for an easy-to-digest explanation of the latest metric-heavy DiD papers.

Andrew C. Baker has notes on Difference-in-Differences Methodology with supporting material on GitHub.

Notes

Paul Goldsmith-Pinkham has a brilliant set of lectures on empirical methods including DiD on GitHub. These are also supplemented by YouTube videos.

Jeffrey Wooldridge has made several notes on DiD which are shared on his Dropbox including Stata dofiles.

Fernando Rios-Avila has a great explainer for the Callaway and Sant'Anna (2020) CS-DID logic on his blog.

Christine Cai has a working document which lists recent papers using different methods including DiDs.

Interactive dashboards

These (related) interactive R-Shiny dashboards showcase how TWFE models give wrong estimates.

Kyle Butts : https://kyle-butts.shinyapps.io/did_twfe/

Hans Henrik Sievertsen : https://hhsievertsen.shinyapps.io/kylebutts_did_eventstudy/

Tweets

Some interesting Twitter threads in no particular sequence. In order to render these properly, you need to view them on the Jekyll website.

I spent this past week catching up with the DiD literature. Here is my list🧵:
1. Read (if you haven't) Andrew Goodman-Bacon's "Difference-in-Differences with Variation in Treatment Timing." The paper that started all for me (there are others before).Link: https://t.co/GBFjBnHDcj

— Jesús Villero (@jotavillero) May 16, 2021

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Navigating the DiD revolution from one applied researcher's perspective.

A LONG 🧵 on what I've learned & what I'm still trying to figure out. Advice/insights welcome!

My @michaelpollan 🥦🍅🥕🫑 inspired TL;DR take:

"Apply DiD in context, not every 2x2, mostly event studies" pic.twitter.com/CWmwyo1Btp

— Matthew A. Kraft (@MatthewAKraft) June 24, 2021

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I've been catching up on staggered diff-in-diff/two-way fixed effects recently. Simulating helped me see how bad TWFE performs with dynamic treatment effects (see those pre-trends). I also tried implementing Sun & Abraham (2020)'s interaction-weighted estimator in Stata pic.twitter.com/FQBCQi0m7d

— Shan Huang (@ShanHuang_ec) June 16, 2020

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The diff-in-diff literature is plagued with the notion that more controls are always better (as long as the treatment effect has 2 stars). Someone please write a version of this @yudapearl paper aimed at the assumptions & examples of DiD. #EconTwitter https://t.co/JfxQHGlxgw

— Arthur Lewbel (@lewbel) July 29, 2021

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This is a really interesting discussion of using potential outcomes to define causal effects. Not sure I agree with the critiques, but they're thought-provoking

key quote: "I don’t like the idea of types defined by something that happens in the future.."https://t.co/AWRK9Qgj4W pic.twitter.com/a1J8pDxfXK

— Peter Hull (@instrumenthull) July 26, 2021

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As I was re-reading this paper recently, I wanted to draw attention to the fact that this paper by Gardner (2021) derives (in appendix B)( the event-specific weights for each event in a "stacked regression" used in Cengiz et al (2019).

1/https://t.co/ucvjcNtq3Q pic.twitter.com/drVnoE1xHb

— Arindrajit Dube (@arindube) July 21, 2021

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