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.

I enjoyed the course and learned basics of causal inference. What I missed was more exercises with R in order to gain more practical understanding of the material. In particular, it would be great to have exercises where you get some dataset and your task is to calculate given causal effect and you need to come up with an approach and to execute it. This would mimic more closely problems that you encounter in practice.

It's really the easiest way to approach Causality someone who is not from a pure Statistics background. The approach here is different from Judea Pearl's book and I think it's justified because this course was not only for computer science students. This course has changed my perspective on how to work with data.

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.

This course is quite useful for me to get quick understanding of the causality and causal inference in epidemiologic studies. Thanks to Prof. Roy.

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.

Excellent course! The lectures are very clear and easy to follow, and Professor Roy is really good at explaining the concepts in a simple way. The assignments in R are helpful for grasping the theoretical concepts. I would specially recommend this course to data scientist, who might be interested in complementing their predictive analytics skills with the the necessary ones to tackle questions about causality.

This course is absolutely worth your time. Professor Roy is thoughtful, deliberate and careful in his presentation. The course provides plenty of worked examples and external references. Course does not skimp on statistical detail (with some minor exceptions). I do recommend following along with a textbook as well as i found this helped me. Thank you Prof. Roy for making this fantastic course available!

This is a great course for anyone interested in learning more about Causality and models for its estimation. I am a physician with limited statistical knowledge, but was able to follow this course with little difficulty, including analysis in R (though I do know how to run STATA and command line). I would recommend this course to anyone interested in performing a propensity matching study.

Taking this course was a great help for me in my work. I was familiar with most of the matching methods but learning about other preprocessing methods and approaches really widened my view on how to decide what is the best way to do causal analysis on observational data. Thank you for using examples also from the field of social sciences. All in all, thank you for making this course!

Over all, this course is extremely helpful for students who are interested in causal inference of observational data. It provides a rather comprehensive list of methods and techniques that we could use to disentangle causal effects, provided with ample supply of exercises and tests. Highly recommended! Will definitely take other courses on similar topics with the same instructor.

After reading Pearl's book, Causal Inference in Statistics, I found this course really put some meat on the bones, reviewing the basics and demonstrating, in a very clear and easy to understand way, how to conduct the analyses and make causal inferences. The examples in R were reasonably easy to follow and reproduce even for someone who has not used R (me).

I work in the field of Marketing, in a company that is actively exploring Causal Inference methods to estimate the impact of ads on the purchase behaviour. This course provided me with a solid understanding through illustrations and examples. This has changed my perception that experiments are the only answer to tease out a causal effect. Thank you Jason.

The content is relaxing and easy to understand, yet extremely useful in real life when you are conducting experiments. The well designed quiz each week only takes a little time, but could help you to diagnose problems and remember the key points. I really love this course.

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.

I was a novice in causal analysis. But I needed some education in counterfactual estimation. This course provided me with the necessary knowledge and tools. I especially enjoyed the matching, IPTW and IV chapters. Thank you!

Works best on double speed (from settings menu of each video). Content is delivered in clear and relatable manner using interesting real world examples.

This course is really fantastic for all levels. Very thorough explanations and helpful illustrations. Many thanks for putting this together!

Well structured to provide solid understanding of fundamentals, good intuition, and a basic view of applying the covered material.

Great introduction. Immediately used new knowledge in current job (marketing data scientist). Recommended course to co-workers.

To those with some advanced statistics background, this would truly be helpful to catch up econometric thought processes.

Not only good for bio stats, it has also profound impact to my understanding of a/b testing in the internet world.

This is an excellent course taught by a very competent professor in a very simple to understand and intuitive way.

Excellent overview on causality inference and handling confounders combined with practical examples and R code.