Great Course, Thank You!

Very interesting studies.

Very well presented.

enjoyed it very much

Enlightening.

wonderful!

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!

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.

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 :)

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.

It is a great course for those who want to better understand how causality works, statistically speaking.

Until the 3rd week the classes are very well exemplified and detailed, great to follow.

Then, it is difficult to follow the explanations, impacts of the models, etc. - a pity.

The interpretation of analysis results, variations and other subtleties is not the focus of the course. If you expect to see analysis and interpretation of results right away, this course is not for you.

The instructor explains the concepts very clearly and the slides/examples are instructive. I enjoyed the course, finished it, and feel that I have a good understanding of the basics of causal inference; good enough to apply the learned techniques in the real world.

The fact that the slides are not made available is a big downside for me. Furthermore, the labs could have come with more instructions for those that have never worked with R before, like me.

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.

Great professor and teaching. This course was a great introduction to causal inference. I remain a little unsatisfied however on a few concepts which I found insufficiently explained. In particular, the link between DAGs & d-separation and the 2nd part of the course is not very well explained. I would recommend to first follow the EdX course "Causal Diagrams: Draw Your Assumptions Before Your Conclusions".

This course covers theoretical and coding parts of causal inference. Assumes no prior knowledge from the user. The coverage of topics as well as the teaching is very good. Removed one star as no slides are provided for this course. If you want to revisit / revise concepts you need to go through video and search them. Also, the community is not active so do not expect your questions to be answered.

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.

The course is an excellent starting point for someone that wants to dive into Causal Inference. After this course, other literature starts to become more accessible.

A drawback is that the programing language used in the examples and on some quizzes are in R. Some of the quizzes are very old, and the given instructions to solve them need to be "bent" a little bit in order to be solved.

Needs to explicity state that R is a requirement to complete course.

Some datasets are outdated from MatchIt package.

Many answers from other students have not been addressed/answered.

Lecturer is obviously very knowledgeable but content can be quite hard to follow. I appreciate that this is quite a difficult course to cover.

The contents covered in the lecture are excellent. I've gained a much better understanding of Causality thanks to this course. The only complaint I have is that the dataset required for the coding assignments has not been updated, and therefore does not have the exact same features as mentioned in the instructions.

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).

A very useful course about causal inference. I read lots of book of casuality, but still think I am not figure out the key points. Through this course, I get the main idea of causal inference and the very practical R code. If we can have more practice and more deep knowledge, it would be better!

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.

Nice and simple to follow introductory course on the topic. My only critic would be relying exclusively on R: some python code (both "by hand" and through some packages) would have been great.

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.

A high quality course that delivers what it says in the title. Well-paced introduction to the potential outcomes framework, with a nice balance of theoretical and practical aspects.