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

4.7
stars
530 ratings

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

WJ

Sep 11, 2021

Great introduction on the causal analysis.The instructor did a great job on explaining the topic in a logical and rigorous way. R codes are very relevant and helpful to digest the material as well.

MM

Dec 27, 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.

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101 - 125 of 166 Reviews for A Crash Course in Causality: Inferring Causal Effects from Observational Data

By Aniket G

Dec 15, 2019

Superb crash course for quickly getting up to speed!

By Zhe C

Apr 21, 2022

I learned a lot from this course! Highly recommend!

By Marriane M

Oct 8, 2019

Very practical for beginners in causal inference

By Min-hyung K

Jun 30, 2017

Thanks so much for providing this great lecture.

By Arka B

May 31, 2018

gives thorough basic intro to causal inference

By Michael S

Jul 7, 2019

Awesome!!! Looking forward to the next one!!!

By Tarashankar B

Sep 8, 2020

Detailed and excellent course on causality

By Pichaya T

Feb 26, 2018

Excellent courses. I gain my expectations.

By Akin A C

Jan 3, 2021

excellent course, very very useful!!

By Takahiro I

Sep 26, 2017

The best lecture series of causality

By Clancy B

Aug 28, 2018

no nonsense, in depth and practical

By Carolina S

May 18, 2021

A very good introduction course.

By Paulo Y C

Aug 2, 2020

intense and well crafted course!

By William L

Apr 3, 2020

wonderful course, very helpful

By Bob H

Oct 19, 2017

Good intro of the techniques.

By Junho Y

Dec 21, 2020

Jason Roy! He is a monster!

By Simon J S

Aug 4, 2023

Great Course, Thank You!

By Xisco B T

May 5, 2019

Very interesting studies.

By Andreas N

Aug 29, 2020

Very well presented.

By Chang L

Sep 11, 2017

enjoyed it very much

By Jose S

Feb 22, 2020

Enlightening.

By Bolin W

Jun 4, 2021

wonderful!

By Alfred B

Nov 22, 2019

Overall a great course. Better than other courses on causal inference on coursera. However, some of the topics (e.g. within the IPTW and IV methodologies ) were presented in a sort of general manner (intuitive). Which is obviously not a fault of the instructor and is due to the strong research nature of these topics. Personally, I can't think of presenting, for instance, 2SLS or insights on IPTW in more detail within a crash course. Perhaps, increasing the number of weeks to 6 or 7 in order to include more detail on, e.g. 2SLS would be a good idea. What definitely helped to make up for those missed details is the practical examples parts with R. Keep up the good job!

By Marko B

Oct 12, 2019

Clear course most of the time and a very interesting subject. The teacher covers the concepts from many angles: conceptual understanding, math, examples and R code. I like how there is little "fluff", you learn a lot for the time given and I don't feel any of the concepts covered are unnecessary or esoteric. The only negative is that the course could've benefited from more practical assignments. There are 2 R code assignments: could've been more. I was thinking about giving it a 5 or 4 stars and decided on 4 in case a non-perfect score actually makes the instructor improve the course.

By Sébastien M

Apr 30, 2022

It was very fluid and well-detailed. The sructure of each video was clear with a lot of nice examples.

However I found the content too much specific (usually on Biological questions), which makes most of the tools used here questionable for others fields. For example, some of my great questions are :

1- How do I estimate causal effect if the treatment is continuous ?

2- What if I have a set of treatments and want to analyse the causal effect of subsets within them ?

It would be nice to take the content of this course on a more general view :)