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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
186 ratings
65 reviews

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

##### MF

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.

##### FF

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.

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## 1 - 25 of 65 Reviews for A Crash Course in Causality: Inferring Causal Effects from Observational Data

By Mateusz K

Dec 07, 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 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 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 Herman S

Oct 03, 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 KATONA N P

Dec 01, 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 04, 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 01, 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 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 HEF

Feb 19, 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 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

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 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 Mark F

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 FKG

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 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 Andrew

May 16, 2018

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

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 Mario M

Jan 12, 2020

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

By Joon-Ku I

Oct 24, 2017

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

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 12, 2017

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

By Michael L

Nov 26, 2017

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

By DR A N

Aug 22, 2017

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

By Luca A

Sep 24, 2019

A clear and straight-to-the-point introduction to causality. I'm really enjoying the course!