Parallel Trends (The Effect, Videos on Causality, Ep 52)

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 18 of the book.

For difference-in-differences to work, you need to make an important assumption: the parallel trends assumption! This assumption says that the change over time you see in the control group is the change over time you *would have seen* for the treated group, if the treatment hadn't occurred. How can we understand this assumption, and how can we make it as plausible as possible?

[mylifeisinhishandsamen]

This is a special request from me....could you please make a video about how to use lead-lag analyses to ascertain parallel trends assumption in R?

[RobertWF]

Thanks Nick! Am I correct that we cannot empirically test the parallel trends assumption because we can't control for all the possible differences between the treatment and control groups? (Although the synthetic control method claims to equalize non-parallel trends.)

[rxsieexxx]

Omg thanks so much for the great video!!