Loupe Copy
Chevron Left
Back to A Crash Course in Causality: Inferring Causal Effects from Observational Data

Learner Reviews & Feedback for A Crash Course in Causality: Inferring Causal Effects from Observational Data by University of Pennsylvania

4.7
stars
186 ratings
•
65 reviews

About the Course

We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). At the end of the course, learners should be able to: 1. Define causal effects using potential outcomes 2. Describe the difference between association and causation 3. Express assumptions with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting) 5. Identify which causal assumptions are necessary for each type of statistical method So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!...

Top reviews

MF

Dec 28, 2017

I really enjoyed this course, the pace could be more even in parts. Sometimes the pace could be more even and some more books/reference material for further study would be nice.

FF

Nov 30, 2017

The material is great. Just wished the professor was more active in the discussion forum. Have not showed up in the forum for weeks. At least there should be a TA or something.

Filter by:

51 - 65 of 65 Reviews for A Crash Course in Causality: Inferring Causal Effects from Observational Data

By Wayne L

•

Mar 17, 2019

Very easy to follow examples and great coverage for such an important topic! The delivery sometimes get repetitive and I wish we talked more about how the uncertainties are derived.

By Alejandro A P

•

Dec 15, 2018

very good content. Story line is highly concise. However, Lecturer could be more stream-lined the the way of explaining. He sure is a skilled guy, however.

By Patrick W D

•

Jul 15, 2018

Excellent course. Could use a small restructuring, as I had to go through the material more than once, but otherwise, very good material and presentation.

By Christopher R

•

Feb 11, 2019

I thought this was a good overview and I'm glad I took the course, but I would have preferred more hands on programming assignments.

By Ruixuan Z

•

Jun 23, 2019

Some of the materials are bit academical and away from industry, however, I found most of the materials relevant and practical.

By Yahia E G

•

Jan 09, 2020

Great course. I have learned a lot. I just wish to have more programming exercises to cement our knowledge.

By Chris C

•

Aug 29, 2018

Could use a bit more guidance on the projects, but overall a helpful course. Gets straight to the point.

By Manuel F

•

Oct 21, 2018

Interesting introductory course about causality. Good "compilation" in just 5 weeks.

Thanks!

By Naiqiao H

•

Feb 27, 2019

The course is very useful for beginners. The materials are clear and easy to understand.

By Fernando V C B

•

Nov 24, 2017

They could offer more applied exercises in R. But, it was also great.

By Juan M C B

•

Oct 07, 2019

Great

By Andrew L

•

Nov 28, 2019

Clear deliver of engaging content. Very disappointed the course lacked an IV program or some capstone to evaluate learning. Why would you complete the course with a quiz compared to a practical assignment. I also do not understand why the slides are not available.

By Ignacio S R

•

Apr 30, 2018

The course is ok, but not having access to the slides is very annoying

By Francisco P

•

May 30, 2019

Hard to understand

By Eva Y G

•

Sep 29, 2019

Can not download slides which make the source material very inaccessible