After some nice #OpenSource collaboration, version 0.3.0 of the #rstats penalised #SyntheticControl package "pensynth" is now available on CRAN! :rstats:
https://cran.r-project.org/package=pensynth
Do you want to use the synthetic control method for #CausalInference with observational data? Try it out!
(⚡ It's faster than vanilla synthetic controls even without penalty)
New :rstats: package! For a project, I had to implement penalized synthetic control estimation, and I thought it would be nice to implement it "properly" so others could use it too.
https://github.com/vankesteren/pensynth
It also has a basic form of cross-validation to automatically determine the penalty parameter.
Feel free to try out / comment / collaborate! :air_quotes_left:
#rstats #statistics #economics #SyntheticControl #PolicyEvaluation #CausalInference
The basic idea of #SyntheticControl for #causalinference is actually really simple.
Find out more in my #pydataglobal talk tomorrow
https://global2022.pydata.org/cfp/talk/FQBSP8/
They basically do the same that I did. They estimate a #SyntheticControl based on binary treatment classification of areas from where tests were dispatched to #Immensa lab.
Its funny how their and my figures a year later are so similar.
It highlights: data access is a key barrier as they had slightly better data. But this was entirely avoidable.
In fact, I did launch a #FreedomOfInformation #FOI request last year to request the data that the #UKHSA team was using...
Now that FOI...
@WeinbergEcon ahah that's fair. Well I also put #syntheticControl, long but unmistakable. The query is not producing much! Maybe some appropriate tags can help. @paulgp @johnholbein @marcfbellemare 🖖
Alright tooter, here comes my first #EconTwitter question.
What is your favourite recent paper applying #SyntheticControl ?
I am currently reading Hansen & McNichols (2020) but am looking for more. Working on a synth project and want to see what's considered best practice in applied settings. Dw, I have the theory side covered!
