Easily the most comprehensible primer on causal inference
Easily the most comprehensible primer on causal inference
This MOOC is more or less my attempt at recreating my econometrics course from university. I started with the assumption that people could learn causal inference with no explicit STEM prerequisites. I wasn’t sure that it could be done, but the feedback I got from some notable professors convinced me that this course may just be one of the most comprehensible primers on the subject.
A technical treatment of causal inference is not what this course is about, and the reason for this is simple. If you are attending university and studying government, sociology, economics or any one of the majors in the social sciences, you will probably be exposed to a technical treatment of the topic during your second or third year of university. And since I believe those university courses to be doing a good job, I do not want to this course to be a lesser substitute.
The ambition of this course is fairly different. Chiefly upheld above all of its ambitions is to be as accessible as possible meaning any one can attend the course material and understand it.
There are absolutely no prerequisites for Causal Beast. I’ve previously taught a derivative of this course to young students in middle school and high school, and they were able to understand all of the core concepts. All that I ask of you to successfully understand causal inference is 1.5 hours of your attention. 
Causal Beast is currently composed of two lessons, each of which is divided into many segments. The first lesson focuses on the foundations of causal inference, and the second lesson focuses on the actual techniques used to infer causality. While it’s advised to go through the course from start to finish according to the outline, if there is a particular topic you’d like to visit, please refer to the search page.
My name is D.H. Kim, and I am a software engineer who is passionate about the intersection of education and technology. I confer most of my free time to creative projects such as producing data visualizations and writing books.
If you found this course to be a useful resource for understanding causal inference, it would mean a lot to me if you shared it with others who could also benefit.
Many thanks to all the people who made creating this course an enjoyable process. Well job done 
Course content including video, notes and code are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.