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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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126 - 150 of 166 Reviews for A Crash Course in Causality: Inferring Causal Effects from Observational Data

By Joe v D

Aug 24, 2017

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.

By Alberto R N

Sep 23, 2020

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.

By Tom v D

Dec 1, 2022

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.

By Manuel A V S

May 6, 2018

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.

By Tanguy d L

Oct 19, 2021

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".

By Haribabu I

Mar 24, 2023

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.

By Varun D N

May 2, 2020

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.

By Tiago F P

Nov 9, 2022

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.

By Ow K W

Feb 18, 2023

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.

By Chi B

Jan 26, 2022

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.

By Michael N

Dec 9, 2018

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).

By James W

Sep 26, 2022

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!

By Osman S

Jun 11, 2020

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.

By Wayne L

Mar 16, 2019

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.

By James C

Nov 21, 2020

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.

By Yi Z

Dec 15, 2021

It will be better to give reviews of related applications in specific AI areas (e.g, computer vision, NLP, etc.) at the end of each of the sections of the lesson.

By Alejandro A P

Dec 15, 2018

very good content. Story line is highly concise. However, Lecturer could be more stream-lined the the way of explaining. He sure is a skilled guy, however.

By Patrick W D

Jul 15, 2018

Excellent course. Could use a small restructuring, as I had to go through the material more than once, but otherwise, very good material and presentation.

By Maxim V

Nov 15, 2021

A consise course on causality; watched on 2x speed because the instructor speaks rather slowly; really bad formatting of quiz questions.

By Christopher R

Feb 10, 2019

I thought this was a good overview and I'm glad I took the course, but I would have preferred more hands on programming assignments.

By Ruixuan Z

Jun 22, 2019

Some of the materials are bit academical and away from industry, however, I found most of the materials relevant and practical.

By Alvaro F

Aug 25, 2020

Great course, the title is exactly what you will get: the basics on inferring causal effects from observational data

By Yahia E

Jan 9, 2020

Great course. I have learned a lot. I just wish to have more programming exercises to cement our knowledge.

By Jeesoo J

Jan 25, 2021

The course is very helpful for beginners to understand. Also, to be able to practice through R is helpful.

By Chris C

Aug 28, 2018

Could use a bit more guidance on the projects, but overall a helpful course. Gets straight to the point.