Causal Inference -- 1/23 -- Basics of Research Design I

This series of online lectures covers the most important causal research designs in economics and other social sciences. This is the first of two videos on the basics of research design.
The course is based on two textbooks:
- Mostly Harmless Econometrics by Joshua Angrist and Steve Pischke (2009): press.princeto...
- Causal Inference -- the Mixtape by Scott Cunningham (2020): mixtape.scunni...
The slides for the course can be downloaded here: 8650ac82-9ca9-...
Course outline:
1 Basics of causal inference (2 videos)
2 Instrumental variables (5 videos)
3 Marginal treatment effects (4 videos)
4 Regression discontinuity and kink designs (4 videos)
5 Difference-in-differences (5 videos)
6 Synthetic control (2 videos)
Get in touch:
- follow me on twitter: / ben_elsner
- website: www.benjaminel...
- profile on linkedin: / benjamin-elsner-b71b98bb

[@julievoyron4947]

This is truly an incredible series of videos!! Thank you so much !

[@bassbass9175]

Thank you for uploading all the lectures. I am planning to go through your lectures this summer with your slides. Again, thank you!

[@gonzaloforgues-puccio789]

You are a very good teacher! Many thanks.

[@kwizeralambert1316]

Thank you so much for these videos. They are very important.

[@kwizeralambert1316]

Greetings from Rwanda. These online lectures are very important, especially development economics, I would like to suggest if these lectures could be demonstrated in programming in R, Stata and Python, and possibly in Julia.

[@ben_elsner]

@@kwizeralambert1316 Just seeing this now. I didn't prepare an implementation section for the online course (I do this in my in-person course at UCD). I recommend that you check the materials provided by Scott Cunningham and Nick Huntington-Klein. On their webpages you should be able to find implementations in Stata, R and Python.

[@kwizeralambert1316]

@@ben_elsner I understand. The work you are doing is amazing. I like your style of teaching. I am learning your lessons. Eager to learn every concept to be able to design a very convincing research. Thanks for the books recommended

[@ben_elsner]

@@kwizeralambert1316 Check out these resources: you find R code for pretty much every technique, nicely explained: mixtape.scunning.com/teaching-resources.html

[@kwizeralambert1316]

@@ben_elsner Thank you so much. I am now with the book. I am studying it.

[@potonsk]

Your lectures are enlightening. Thank you so much for the course!

[@hasibniaz9916]

Thanks Sir

[@yechunhui]

thank you

[@ludo3941]

If I understood correctly the final section of your video, we define the relations we believe there are in the world and then we use normal stathistical methods of calculating correlation. Then, considering our DAG definition and correctly stratifying the data, the "correlation" we calculated is the actual relationship of the data. If that's correct, could you point to a paper or section of those textbooks that says this?

[@ben_elsner]

That's one way of using DAGs, correct. It is difficult, however, to include all possible relationships in a DAG, which is why we often use DAGs to clarify why we should or shouldn't adjust for certain variables (i.e. distinguish between confounders, mediators and colliders), but we would exploit natural experiments that cut through most the confounders. For a more detailed explanation I recommend Nick Huntington-Klein's book The Effect. He explains in great detail and with many examples how one uses DAGs in practice.

[@ludo3941]

@@ben_elsner Tyvm