Visual Logic of Instrumental Variables: Causal Inference Bootcamp

[@bettyaugust5623]

A lecturer that talks in a human way without unnecessary termology and actually explains concepts in a concise manner - THANK YOU FOR THIS SERIES!

[@snoringinseattle]

I am a UNC graduate student and I've been trying to grasp this concept from many different sources. This is the clearest of them all. I'm finally confident that I understand what iv is. Thank you professor!

[@thonghuynh7962]

The best lecturer I ever had

[@lizou761]

Very nice video! Another great book on this topic is "Mastering Metrics.“ Thanks

[@oliviat978]

Awesome examples. Thanks so much!

[@joelrick1629]

Awesome work !

[@arunaugustine568]

Super helpful video!!!

[@camilafloressanhueza7966]

Extremely helpful!!! Thank u very much

[@googledude5649]

So the errorterm in this case is the pre-treatment variables ?

[@LaminManjang-xx6dk]

how many subjects have do and how many subject are compousery to do

[@LosMengers]

¿Qué canción usan al inicio de sus videos?

[@xavsaboss]

nice trim

[@diegomabrary4974]

I think there are two points that I am not agree with. One: randomize Instrument variable only remove the arrow from the confounders to Instrument, does not remove the backward dir. Two: Instrument variable does have some causal effect on the outcome, but not directly impact on the outcome. @author