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.

The course is very simply explained, definitely a great introduction to the subject. There are some missing links, but minor compared to overall usefulness of the course.

Nice course. One can finish it much quicker.

In the last chapter, one could use the AER R package instead the outdate ivpack . the code is still valid

The material is useful and well-presented by Prof. Roy. Although recipes are provided for solving relevant problems in R, more familiarity with R will be required for applying them. Students should be prepared to develop that familiarity on their own.

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!

The course material is excellent, but the course description dramatically under-estimates the study time needed to complete the course. This is especially true for the R assignments if you are not already *very* comfortable in R. There are also many problems with link rot and software/version compatibility issues for the R exercises.

I would have given the course 4 stars were it not for the unforgiving nature of the R exercises.

Overall, I would recommend this course for someone if they are already quite comfortable in R, or are willing to pout in at least 20 hours of work for each of the R assugnments.

This is a very theoretical course with much math formula and less well-explained practical examples to better illustrate those formula. I came to this course hoping to learn about new ideas and techniques of experiment design for causal effect when randomized experiments are not possible. Unfortunately I did not achieve this goal. This is just my personal view. If you come with a different purpose, you might find this course more useful than I did.

I expected more from this course. It gets too deep into the more advanced topics without using specific examples to showcase the main ideas. The instructor could also be more engaging, I had to watch the videos at x1.25 to be able to keep my attention on them

I am sure the instructor is very knowledgeable and excellent in front of a class. His style does not work online.

I thought that this was really an excellent course. Although it was presented solely as lecture slides, the professor really took great care in presenting motivations for techniques and then the techniques themselves - as well as conceptual material (e.g., potential vs. actual outcomes, conditioning vs. setting, etc.) that were crucial to grasping the big picture. My only, minor, suggested tweak might be to possibly make the quizzes a little more challenging - perhaps by giving some numerical problems to solve. But overall, really just a great class.

Coming from an economics background, I really enjoyed seeing how causal inference is being approached in a different field. While the methods used are generally the same, the motivation of these methods or the focus on certain tools and aspects sometimes appears to differ. That really gave me a new perspective on some of the methods in my causal inference toolkit. Good course!

A very good course on causality, really gave solid foundations to tackle other causality methods, I like the fact that the instructor repeats, so as the student to not forget things or even correlate between topics, I wish there was another course showing how to use say Double ML or CasualForests, or in general the ins and outs of using EconML, CasualML and/or DoWhy. Thanks!

This course helped me secure a beachhead in the realm of Causal Inference. My background is in computer science and machine learning. I was struggling with all the terms used in Causal Inference. It is a fascinating topic and this course provides well connected, solid explanations of terms, theory and its application using R. Thank you.

A perfect course for starting with Causal inference. The course is full with many real world examples to help understand the concepts and applications. Not too mathematical and not too epidemiologic either which makes it perfect to understand an to follow. Thank you for such a nice introduction to causal inference.

This course is excellent. The quiz helps to make sure you get the key assumptions and method ideas right, while the programming exercises ensure that you know how each method works and how they can be implemented either manually or by using some of the available statistical R packages for causal effect estimation.

A great course in causality! I strongly recommend it to someone seeking to understand the foundations and assumptions in causal effect estimation. The professor explains the material at a good and understandable pace. I wish we had a follow-up course with more advanced use cases and other projects.

This course does a fantastic job of balancing the theoretical and practical aspects of causal inference. Additionally, it takes the student through three very different techniques of causal inference that apply to common real-world situations in a relatively short course.

One of the best courses I have taken on Coursera. Dr. Jason Roy's knowledge is second to none.

His explanation of the course makes it so much easier to understand the concept. Wish more courses to be offered by him.

Great Job, I have learned and enjoyed the course so much!

This is an excellent course. I audited the because I wanted to learn more about marching and prospensity score and it was awesome. The explanation is quite easy to understand. I would recommend the course to anyone who wants to learn casual inference.

Enjoy

This course is a short one, but power-packed. It gives a different dimension of understanding the data, it's linkages and further extrapolations. Each word of Jason has to be heard properly as he continues to explain facts in a very lucid manner.

This is a very good course to take if you want to get important causal inference methods concepts. Even though it has some math concepts, the Professor does a good job of introducing them really well for a beginner. I would strongly recommend!

Good introductory course. I wish there were more quizzes (at least another 2 more), testing our knowledge of various formulae for computing IPTW (inverse probability of treatment weights), ITT (intent to treat) and at least one more lab in R

One of the best courses in Coursera, Professor with lots of experience in a backpack show how to tackle very complex problem of causal inference. This is a topic every data analyst should know doesn't matter which industry you work or learn.

It is a very useful course that combines conceptual and technical aspects of Applied Causal Inference.

The presentations are very clear, the Examples and Exercises (R-coded) have been very useful for me to practice specific R-packages.

Great course, especially if you are reasonably familiar with R and basic stats and interested in approaching causal analysis. Word of caution: If you have never used R, you will have trouble getting through some of the assignment.s