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

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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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51 - 75 of 166 Reviews for A Crash Course in Causality: Inferring Causal Effects from Observational Data

By Keshab S

Apr 4, 2021

My work involves working with observational data. This course taught me to think in more formal and organized way on topics and questions of causal inference.

By ALEXANDER G

Feb 18, 2022

Great introduction to the field covering model synthesis of causality ideals. Glitches in assignments - make sure to check the discussion for workarounds.

By Radoslav T (

Apr 16, 2023

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

By Giulio B

Mar 12, 2021

Excellent video lectures. Challenging end of module quizzes. I found more challenging doing the practical exercises because I had no experience with R.

By Георгий А

Dec 15, 2021

A very thorough and pleasant intro into the topic. Thanks from Russia! To the lecturer - be more confident in yourself! You are great at your stuff :)

By Oksana B

Nov 28, 2021

Great course! I am glad i came accross it. Helped me a great deal with my project at work. I wish there were more courses by this professor.

By Andrew

May 15, 2018

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

By Yi H

Aug 27, 2022

This course is very helpful for people to understand basics of causual inference with clear explaination and rich real-world examples.

By Сергей М

May 24, 2021

Очень лаконичный и полезный курс. Очень помог разобраться в теме Causal Inference. Отлично подходит для начала вхождения в данную тему.

By Emilio

Jul 26, 2023

Great course. Very clear and practical.

Personally I would have preferred coding and exercises in Python, but overall a great course.

By Ted L

Aug 24, 2019

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

By Kin H L L

Mar 11, 2022

Covered from mathematical concepts to practical statistical analysis with R. A perfect course for newcomers on causal inference.

By Mario M

Jan 12, 2020

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

By Gabriel V

Oct 8, 2022

I was so glad to do this course, it was really helpful for me. How do I make a Citation APA from this Course? Thanks lot.

By JK

Oct 24, 2017

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

By Akorlie A N

Dec 28, 2020

Excellent course. This course helped me to develop my intuition on some of the more abstract concepts in causality.

By Hao L

Aug 31, 2017

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

By Abdulaziz T B

Aug 11, 2017

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

By Vipul J

Dec 25, 2022

This course is excellent at laying the foundations for casualty. Only con is that the slides cannot be downloaded

By Georges A

Dec 20, 2020

Excellent course, extremely well presented that helps clarify a lot of statistical concepts in an intuitive way.

By Minha H

Jul 3, 2021

Good course to review key techniques in causal inference. Would be nice to have more in-depth course in sequel.

By Deleted A

Nov 26, 2017

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

By 朱永載

Jul 25, 2022

Good explanation and hands-on R practice.

Highly recommended for those working on the observational studies

By Akshay N

Aug 22, 2017

Excellent course! Can make it longer though and cover more details and latest advances and issues :-)

By Dror G

Jan 18, 2021

Very enlightening. Well explained, and strikes a great balance between theory & practical aspects.