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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
560 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 ...
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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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176 - 181 of 181 Reviews for A Crash Course in Causality: Inferring Causal Effects from Observational Data

By Kasra S

Aug 14, 2021

I think there are parts in the course where further discussion is needed.

By Ignacio S R

Apr 30, 2018

The course is ok, but not having access to the slides is very annoying

By L L

Nov 5, 2024

good material, but lecturer is not very engaging

By Francisco P

May 30, 2019

Hard to understand

By coursera s

Jan 8, 2025

Insufficient replies to student questions in discussion and following code and lectures didn't provide enough information to complete the programming assignment. I ended up guessing until I got through the assignment. pointless waste of time.

By Eva Y G

Sep 28, 2019

Can not download slides which make the source material very inaccessible