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

4.7
stars
530 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

WJ

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.

MM

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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26 - 50 of 166 Reviews for A Crash Course in Causality: Inferring Causal Effects from Observational Data

By Morbo

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.

By lorenzo c

Apr 9, 2021

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.

By Steven G

Sep 29, 2020

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.

By 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!

By Scott M

Feb 16, 2022

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.

By Siyu H

Feb 14, 2021

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.

By Carla F G

Oct 10, 2021

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

By Haim T

Apr 1, 2021

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

By Florian C

Sep 30, 2021

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!

By seyed r m

May 21, 2022

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.

By A M

Jul 27, 2021

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.

By Anthony M

Aug 26, 2021

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.

By Albert L

Mar 26, 2023

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!

By Adeyemo o m

Apr 16, 2022

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

By Piyush J

Apr 14, 2020

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.

By Frank O

Nov 21, 2021

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!

By Vikram M

May 30, 2019

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

By Vlad

Apr 20, 2018

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.

By Hugo E R R

Jan 20, 2021

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.

By Pritish K

May 16, 2020

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

By Arnab S

Nov 24, 2017

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!

By Alice G

Feb 21, 2021

Really wonderful course--I learned so much in the way of theory and practical application in R. Some links need to be updated and it would be best to provide students with answers to worked examples for the quiz questions.

By Weifeng J

Sep 12, 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.

By Fang W

May 23, 2023

Great class! I have learned a lot on causal inference to conduct experiment analysis at work. The R coding sessions and lectures on the logic/math behind are really helpful.

By Anastasia G

Feb 21, 2021

A great start for those starting to explore causal inference. The somewhat dry delivery of the lectures is fully compensated by how clear and informative they are.