Last updated: 17 July 2021.
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. Let's make this the most epic DiD repository ever!
TODO:
- Dump the info.
- Add key lit.
- Port to a proper website.
TO BE ADDED
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 fromhttps://simonfreyaldenhoven.github.io/software/
stackedev
:net install stackedev, from("https://raw.githubusercontent.com/joshbleiberg/stackedev/main/")
TO BE ADDED.
SORTABLE TABLE TO BE ADDED. In alphabetical order by last name.
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.
Scott Cunningham (2020). Causal Inference: The Mix Tape.
Nick Huntington-Klein (2021). The Effect.
Here are people who are actively involved in curating information on the latest DiD developments. This includes blogs, lecture series, tweets.
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. Will post links if recordings are up somewhere.
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.
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.
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.
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/
Twitter threads that summarize the DiD literature. In order to render these properly, you need to view them on the Jekyll website.
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>I spent this past week catching up with the DiD literature. Here is my list🧵:
— Jesús Villero (@jotavillero) May 16, 2021
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
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>Navigating the DiD revolution from one applied researcher's perspective.
— Matthew A. Kraft (@MatthewAKraft) June 24, 2021
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
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>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