Back to A Crash Course in Causality: Inferring Causal Effects from Observational Data

4.7

stars

186 ratings

•

65 reviews

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!...

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.

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.

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By Cameron F

•Apr 05, 2019

Good course on the over view of Causality. Not too technical, but not too light and fluffy.

By Akash G

•Jun 17, 2018

Amazing Course! Really Helpful. I would love to have a similar full-duration course :D

By Jiacong L

•Nov 27, 2019

I learned so much from Dr. Roy by watching his great lectures. Thank you!

By 张采蔚

•Aug 04, 2019

This course is very suitable for beginners, clear and easy to understand.

By Vikram R

•Mar 14, 2018

Great course for getting your hands dirty with some real causal methods.

By olufemi B o

•Aug 23, 2019

The course itremendoulsy straightened my knowledge of causal evaluation

By Bob K

•Oct 16, 2018

Well taught, easy to follow but potentially very important techniques

By Rudy M P

•Apr 17, 2018

I learned the basics of causality inference and want even more now!

By Aniket G

•Dec 16, 2019

Superb crash course for quickly getting up to speed!

By Marriane M

•Oct 08, 2019

Very practical for beginners in causal inference

By Min-hyung K

•Jul 01, 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 08, 2019

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

By Pichaya T

•Feb 26, 2018

Excellent courses. I gain my expectations.

By Takahiro I

•Sep 26, 2017

The best lecture series of causality

By clancy b

•Aug 29, 2018

no nonsense, in depth and practical

By Bob H

•Oct 20, 2017

Good intro of the techniques.

By Xisco B

•May 05, 2019

Very interesting studies.

By Chang L

•Sep 11, 2017

enjoyed it very much

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 Theo B

•Jul 02, 2017

In the beginning the course to me was quite difficult, as it has a different perspective on statistics I was used to. Most people tend to say: "correlation is not causality". When it came to propensity scores, matching and so on the possibilities became more clear to me to apply these methods in practice. The pace of the videos is slow, so I played the videos in 1.5 of the time. What I missed was the ability to download the slides. The instructor would look into this, but we're still waiting several weeks later. Another thing I missed was any sense how many other students were in the course.

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 Joe v D

•Aug 25, 2017

Very approachable as someone with a Masters in Statistics, probably tough if you are not comfortable with notation and concepts of intermediate prob/stats. Extremely clear and concise presentation. Coverage of methodology is a little weak, there is not enough discussion of the dangers of doing causal inference on observational data, nor of the dangers of the proposed methods. For instance, propensity score matching is ineffective or even harmful in the face of hidden confounders, which in the real world you almost always have.

By Manuel A V S

•May 06, 2018

I have an economics background and during my undergraduate studies I took several statistics and econometric courses. The contents delivered in this course complemented my knowledge very well from another point of view. I would definitely enjoy a more advanced course dealing with other methods. The only aspect I would improve is providing the slides for further study. Other courses in Coursera do this and, honestly, I often consult the slides.

By Michael N

•Dec 09, 2018

Content was useful for understanding causal inference in a variety of situations. Presentation was sometimes slow even on double-speed. Lectures were generally structured from abstract to concrete, which was much harder to follow than if it were presented in english first and then made abstract (Mayer, 2009).

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