Update README.md

README.md

@@ -160,7 +160,7 @@ These (related) interactive R-Shiny dashboards showcase how TWFE models give wro
 
 ## Tweets
 
-Twitter threads that summarize the DiD literature. In order to render these properly, you need to view them on the [Jekyll website](https://asjadnaqvi.github.io/Diff-in-Diff-Notes/).
+Some interesting Twitter threads. In order to render these properly, you need to view them on the [Jekyll website](https://asjadnaqvi.github.io/Diff-in-Diff-Notes/).
 
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@@ -176,3 +176,7 @@ Twitter threads that summarize the DiD literature. In order to render these prop
 <blockquote class="twitter-tweet"><p lang="en" dir="ltr">I&#39;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 &amp; Abraham (2020)&#39;s interaction-weighted estimator in Stata <a href="https://t.co/FQBCQi0m7d">pic.twitter.com/FQBCQi0m7d</a></p>&mdash; Shan Huang (@ShanHuang_ec) <a href="https://twitter.com/ShanHuang_ec/status/1272928307441475585?ref_src=twsrc%5Etfw">June 16, 2020</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
 
 
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+<blockquote class="twitter-tweet"><p lang="en" dir="ltr">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 <a href="https://twitter.com/yudapearl?ref_src=twsrc%5Etfw">@yudapearl</a> paper aimed at the assumptions &amp; examples of DiD. <a href="https://twitter.com/hashtag/EconTwitter?src=hash&amp;ref_src=twsrc%5Etfw">#EconTwitter</a> <a href="https://t.co/JfxQHGlxgw">https://t.co/JfxQHGlxgw</a></p>&mdash; Arthur Lewbel (@lewbel) <a href="https://twitter.com/lewbel/status/1420725923922468866?ref_src=twsrc%5Etfw">July 29, 2021</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
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