Interrupted Time Series (The Effect, Videos on Causality, Ep 49)

Please visit www.theeffectbook.net to read The Effect online for free, or find links to purchase a physical copy or ebook.
The Effect is a book about research design and causal inference. How can we use data to learn about the world? How can we answer questions about whether X causes Y even if we can't run a randomized experiment? The book covers these things and plenty more. These videos are meant to accompany the book, although they can also be viewed on their own.
This video relates to material found in Chapter 17 of the book.
A version of this video without background music can be found here: • Interrupted Time Serie...
One way to implement an event study is as an interrupted time series. In its simplest form, this just means fitting a linear time trend to either side of the event and seeing what changes at the time the event goes into effect!

[@davogaribay]

Congratulations! This is an awesome job and also it is super clear to follow and understand. I just had a glimpse of the book and it also looks very clear and straightforward

[@MrHangover53]

Hi Prof. Huntington-Klein, Many many thanks for the book and all the videos. I still do not understand how the ITS approach is applied in R Also the Taljaard et al. paper does not give any replication material . Should it be : mod1

[@NickHuntingtonKlein]

Subtracting the date like that won't work (should be as.Date('2019-02-15')), but other than that, yep!

[@MrHangover53]

@@NickHuntingtonKlein Thank you very much, cheers!

[@atiyaabdulkarim716]

Hi, Do you add co-variates in your model? If so, do you put covariates measured at baseline?

[@NickHuntingtonKlein]

Since in ITS the treatment applies to everyone at the same time, baseline-measured covariates can't be a source of confounding, so adding covariates won't solve any causal inference issue. But you can add them to improve predictive power and reduce noise. Covariates that hcange over time might in some cases be necessary to solve causal inference issues, but you need to be careful with these to avoid issues like post-treatment bias.