A Crash Course in Causality: Inferring Causal Effects from Observational Data(으)로 돌아가기

4.7

별점

270개의 평가

•

88개의 리뷰

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!...

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.

필터링 기준:

교육 기관: Min-hyung K

•Jul 01, 2017

Thanks so much for providing this great lecture.

교육 기관: Arka B

•May 31, 2018

gives thorough basic intro to causal inference

교육 기관: Michael S

•Jul 08, 2019

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

교육 기관: Tarashankar B

•Sep 08, 2020

Detailed and excellent course on causality

교육 기관: Pichaya T

•Feb 26, 2018

Excellent courses. I gain my expectations.

교육 기관: Takahiro I

•Sep 26, 2017

The best lecture series of causality

교육 기관: Clancy B

•Aug 29, 2018

no nonsense, in depth and practical

교육 기관: Paulo Y C

•Aug 02, 2020

intense and well crafted course!

교육 기관: William L

•Apr 03, 2020

wonderful course, very helpful

교육 기관: Bob H

•Oct 20, 2017

Good intro of the techniques.

교육 기관: Xisco B

•May 05, 2019

Very interesting studies.

교육 기관: Andreas N

•Aug 29, 2020

Very well presented.

교육 기관: Chang L

•Sep 11, 2017

enjoyed it very much

교육 기관: Jose S

•Feb 22, 2020

Enlightening.

교육 기관: 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!

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: Varun D N

•May 03, 2020

The contents of this course are extremely concise and useful. The course prioritizes some of the important techniques used for causal inference. The practice tests , quizzes and data analysis tests were helpful to learn better. The lectures weren't inspiring or exciting and self-motivation is necessary to be able to stick with it. However, I would recommend this course to anyone interested.

교육 기관: 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).

교육 기관: Osman S

•Jun 11, 2020

The course is well structured and the slides are well prepared. Professor clearly explains the formulas and makes you easily understand everything that is written on the slides. However, I would love to see some more examples from the social sciences.

교육 기관: Cesar Y

•Aug 31, 2020

Course is great for a general overview! That said, the discussion forums are poorly monitored and one of the exercise datasets needs to be updated. In any case, don't expect more from a Coursera course!

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.