Regression Discontinuity Design and Instrumental Variables | Causal Inference in Data Science Part 4

This is our last video of the mini course on application of Causal Inference in data science. We covered the complications of Regression Discontinuity Design in reality. We also talked about some other forms of natural experiment. At the end, Yuan shares a handful of resources for you to further study.
🔗 Slides by Yuan: rdd-demo.netlify.app/#/0/14/0
🔗 Regression and Matching • Regression and Matchin...
🔗 Difference-in-differences and Synthetic Control • Difference-in-differen...
🔗 Regression Discontinuity Design • Regression Discontinui...
📃 Yuan's blog post on causal inference www.yuan-meng.com/posts/causa...
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====================
Contents of this video:
====================
00:00 Introduction
1:08 Complications of RDD
7:08 Instrumental Variables
12:26 Summary
18:20 Resources

[@eitoruchan]

Emma, I have to say the work you do is simply amazing. I’m working on my master degree’s thesis, which is on causality, and these videos with Yuan have been beyond helpful - not to mention that I owe you the last job I got, as I watched your channel day and night when I was preparing for the interviews :) congrats and keep up with the good work!

[@xinyaohui1919]

thanks a lot!!

[@arunabhsingh5448]

Your channel is so underrated but so amazing

[@evasun531]

This is great content! I know it's pretty hard for us non-native speakers, but I notice there're a lot of filling words which impact the flow of learning. If this can be improved this video will be awesome!

[@emma_ding]

Thanks a lot for the feedback, Eva!

[@Stanmay1]

Hi! All of your videos are super helpful. Could you please make a video on the incremental effects of marketing interventions?

[@emma_ding]

Great suggestion!

[@karad.2473]

I didn't quite get making the birth season as the IV for school year, why don't we use a binary variable as finished high school or not instead? A bit confused on why we need a IV here and what does "reduced form", "1st stage" mean in the formula?

[@omersarigul92]

thanks a lot