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Learner Reviews & Feedback for A Crash Course in Causality: Inferring Causal Effects from Observational Data by University of Pennsylvania

541 ratings

About the Course

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!...

Top reviews


Sep 11, 2021

Great introduction on the causal analysis.The instructor did a great job on explaining the topic in a logical and rigorous way. R codes are very relevant and helpful to digest the material as well.


Dec 27, 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.

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1 - 25 of 171 Reviews for A Crash Course in Causality: Inferring Causal Effects from Observational Data

By Fred

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.

By Pak S H

Sep 7, 2020

I completed all 4 available courses in causal inference on Coursera. This one has the best teaching quality. The material is very clear and self-contained!

By Anna B

Mar 17, 2020

Excellent introduction course. Jason Roy is an incredibly talented teacher.

By Kilder U

Nov 7, 2020

A must for anyone interested in causal effect estimation. The professor is throughout with the content, he doesn't go too fast and too slow, and relies heavily in explaining the intuition behind the methods. It'd be great if he could do a second course on this with the more advanced topics mentioned but left out, like sensitivity analysis for propensity score, IPTW and IV, that are requiered for those writing papers.

By Mateusz K

Dec 7, 2018

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.

By Sam P

Oct 4, 2020

Fantastic instructor with lessons accessible for both those with some background wanting to brush up and for newcomers. Note that the programming assignments are in R and one uses a fixed random seed so it will be difficult to complete the assignments in another language. That said, the data are available so you can play with the same concepts in another language outside of the assignments. Certainly recommend.

By Miguel B

Apr 17, 2018

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.

By Dr. C C

Mar 20, 2021

This is one of the best online course I have taken so far, Jason is a very good instructor and he explains everything clearly in an easy to understand manner. I have tried another course on a similar topic on Coursera but I simply gave up on the other one. This course provides concrete examples and exercises, it allows me to understand the topic in fine details. I highly recommend this course.

By Oliver D

Jul 30, 2020

I enjoyed the course a lot and I think I took a lot from it as well. The quizzes and computer projects were appropriate, and the resourcees posted were very useful.

By charlene e

Jul 16, 2017

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

By Wei F

Nov 25, 2018

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

By Jiacong L

Nov 27, 2019

I learned so much from Dr. Roy by watching his great lectures. Thank you!

By Theo B

Jul 2, 2017

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.

By Wenjie Z

Mar 25, 2023

Brief yet comprehensive crash course in causality, which introduces the controlling vs. setting, causal assumptions, incident user design, active comparator design, directed acyclic graph (DAG), backdoor path criterion and disjunctive cause criterion for selecting variables to control for, propensity score matching, IPW matching, doubly robust estimators, instrumental variable analysis, two-stage least squares (2SLS), intention-to-treat (ITT) analysis, as well as sensitivity analysis, with R packages and scripts available! Super helpful and highly recommended!

By Categorical S

Mar 29, 2020

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!

By Herman S

Oct 2, 2017

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.

By Nóra P K

Dec 1, 2019

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!

By Leihua Y

May 12, 2019

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.

By Stephen M D

Sep 4, 2019

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).

By Benjamin R

Sep 1, 2019

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.

By Seana G

May 4, 2020

I really enjoyed this course. The pace was great for completing while also working. I found the lectures a good length and the worked examples were really useful, as were the data analysis assignments. I was able to apply the learning directly as a reviewer for a manuscript asked for matched analyses, so that was great. Highly recommend.

By Ayush T

Jan 17, 2020

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.

By Ali A A M

Feb 15, 2021

Excellent course and lecturer. The lecturer takes his time to explain everything in a smooth speed. Is easy to follow. Good exercises and quizzes. I am quite satisfied with the course. I am looking for more (advance) courses from the same lecturer about the same subject, but also other subjects.


Feb 18, 2019

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.

By Srinidhi M

Sep 11, 2023

Excellent course. Builds a solid foundation from first principles. Should be a required course for anyone working as an applied statistician or data scientist. Most data science/ machine learning courses ignore causality altogether which is a real shame.