返回到 A Crash Course in Causality: Inferring Causal Effects from Observational Data

4.7

177 個評分

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61 個審閱

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.

篩選依據：

創建者 Manuel F

•Oct 21, 2018

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

Thanks!

創建者 Chris C

•Aug 29, 2018

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

創建者 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.

創建者 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.

創建者 Juan M C B

•Oct 07, 2019

Great

創建者 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!

創建者 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.

創建者 Ignacio S R

•Apr 30, 2018

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

創建者 Francisco P

•May 30, 2019

Hard to understand

創建者 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.

創建者 Eva Y G

•Sep 29, 2019

Can not download slides which make the source material very inaccessible