A Crash Course in Causality: Inferring Causal Effects from Observational Data
FREE
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! The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth–oldest institution of higher education in the United States, and considers itself to …
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Specification: A Crash Course in Causality: Inferring Causal Effects from Observational Data

96 reviews for A Crash Course in Causality: Inferring Causal Effects from Observational Data
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Price  Free 

Provider  
Duration  27 hours 
Year  2017 
Level  Intermediate 
Language  English 
Certificate  Yes 
Quizzes  Yes 
FREE
FKG –
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.
Mark F –
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.
Pichaya T –
Excellent courses. I gain my expectations.
Vikram R –
Great course for getting your hands dirty with some real causal methods.
Miguel B –
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.
Rudy M P –
I learned the basics of causality inference and want even more now!
Miguel B –
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.
Rudy M P –
I learned the basics of causality inference and want even more now!
Vlad V –
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.
Vlad V –
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.
Ignacio S R –
The course is ok, but not having access to the slides is very annoying
Ignacio S R –
The course is ok, but not having access to the slides is very annoying
Manuel A V S –
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.
Manuel A V S –
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.
Andrew –
This course is really fantastic for all levels. Very thorough explanations and helpful illustrations. Many thanks for putting this together!
Andrew –
This course is really fantastic for all levels. Very thorough explanations and helpful illustrations. Many thanks for putting this together!
Arka B –
gives thorough basic intro to causal inference
Arka B –
gives thorough basic intro to causal inference
Akash G –
Amazing Course! Really Helpful. I would love to have a similar full duration course 😀
Akash G –
Amazing Course! Really Helpful. I would love to have a similar full duration course 😀
Patrick W D –
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.
Patrick W D –
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.
Chris C –
Could use a bit more guidance on the projects, but overall a helpful course. Gets straight to the point.
clancy b –
no nonsense, in depth and practical
Chris C –
Could use a bit more guidance on the projects, but overall a helpful course. Gets straight to the point.
clancy b –
no nonsense, in depth and practical
Bob K –
Well taught, easy to follow but potentially very important techniques
Bob K –
Well taught, easy to follow but potentially very important techniques
Manuel F –
Interesting introductory course about causality. Good “compilation” in just 5 weeks. Thanks!
Manuel F –
Interesting introductory course about causality. Good “compilation” in just 5 weeks. Thanks!
Wei F –
This course is quite useful for me to get quick understanding of the causality and causal inference in epidemiologic studies. Thanks to Prof. Roy.
Wei F –
This course is quite useful for me to get quick understanding of the causality and causal inference in epidemiologic studies. Thanks to Prof. Roy.
Mateusz K –
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.
Mateusz K –
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.
Michael N –
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).
Michael N –
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).
Alejandro A P –
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.
Alejandro A P –
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.
Christopher R –
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.
Christopher R –
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.
HEF –
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.
HEF –
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.
Naiqiao H –
The course is very useful for beginners. The materials are clear and easy to understand.
Naiqiao H –
The course is very useful for beginners. The materials are clear and easy to understand.
Wayne L –
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.
Wayne L –
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.
Cameron F –
Good course on the over view of Causality. Not too technical, but not too light and fluffy.
Cameron F –
Good course on the over view of Causality. Not too technical, but not too light and fluffy.
Xisco B –
Very interesting studies.
Xisco B –
Very interesting studies.
Leihua Y –
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.
Leihua Y –
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.
Vikram M –
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
Francisco P –
Hard to understand
Vikram M –
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
Francisco P –
Hard to understand
Ruixuan Z –
Some of the materials are bit academical and away from industry, however, I found most of the materials relevant and practical.
Ruixuan Z –
Some of the materials are bit academical and away from industry, however, I found most of the materials relevant and practical.
Michael S –
Awesome!!! Looking forward to the next one!!!
Michael S –
Awesome!!! Looking forward to the next one!!!
olufemi B o –
The course itremendoulsy straightened my knowledge of causal evaluation
olufemi B o –
The course itremendoulsy straightened my knowledge of causal evaluation
Ted L –
Well structured to provide solid understanding of fundamentals, good intuition, and a basic view of applying the covered material.
Ted L –
Well structured to provide solid understanding of fundamentals, good intuition, and a basic view of applying the covered material.
Benjamin R –
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.
Benjamin R –
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.
Stephen M D –
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).
Stephen M D –
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).
Luca A –
A clear and straight to the point introduction to causality. I’m really enjoying the course!
Luca A –
A clear and straight to the point introduction to causality. I’m really enjoying the course!
Eva Y G –
Can not download slides which make the source material very inaccessible
Eva Y G –
Can not download slides which make the source material very inaccessible
Juan M C B –
Great
Juan M C B –
Great
Marriane M –
Very practical for beginners in causal inference
Marriane M –
Very practical for beginners in causal inference
Marko B –
Clear course most of the time and a very interesting subject. The teacher covers the concepts from many angles: conceptual understanding, math, examples and R code. I like how there is little “fluff”, you learn a lot for the time given and I don’t feel any of the concepts covered are unnecessary or esoteric. The only negative is that the course could’ve benefited from more practical assignments. There are 2 R code assignments: could’ve been more. I was thinking about giving it a 5 or 4 stars and decided on 4 in case a non perfect score actually makes the instructor improve the course.
Marko B –
Clear course most of the time and a very interesting subject. The teacher covers the concepts from many angles: conceptual understanding, math, examples and R code. I like how there is little “fluff”, you learn a lot for the time given and I don’t feel any of the concepts covered are unnecessary or esoteric. The only negative is that the course could’ve benefited from more practical assignments. There are 2 R code assignments: could’ve been more. I was thinking about giving it a 5 or 4 stars and decided on 4 in case a non perfect score actually makes the instructor improve the course.
Alfred B –
Overall a great course. Better than other courses on causal inference on coursera. However, some of the topics (e.g. within the IPTW and IV methodologies ) were presented in a sort of general manner (intuitive). Which is obviously not a fault of the instructor and is due to the strong research nature of these topics. Personally, I can’t think of presenting, for instance, 2SLS or insights on IPTW in more detail within a crash course. Perhaps, increasing the number of weeks to 6 or 7 in order to include more detail on, e.g. 2SLS would be a good idea. What definitely helped to make up for those missed details is the practical examples parts with R. Keep up the good job!
Alfred B –
Overall a great course. Better than other courses on causal inference on coursera. However, some of the topics (e.g. within the IPTW and IV methodologies ) were presented in a sort of general manner (intuitive). Which is obviously not a fault of the instructor and is due to the strong research nature of these topics. Personally, I can’t think of presenting, for instance, 2SLS or insights on IPTW in more detail within a crash course. Perhaps, increasing the number of weeks to 6 or 7 in order to include more detail on, e.g. 2SLS would be a good idea. What definitely helped to make up for those missed details is the practical examples parts with R. Keep up the good job!
Jiacong L –
I learned so much from Dr. Roy by watching his great lectures. Thank you!
Jiacong L –
I learned so much from Dr. Roy by watching his great lectures. Thank you!
Andrew L –
Clear deliver of engaging content. Very disappointed the course lacked an IV program or some capstone to evaluate learning. Why would you complete the course with a quiz compared to a practical assignment. I also do not understand why the slides are not available.
Andrew L –
Clear deliver of engaging content. Very disappointed the course lacked an IV program or some capstone to evaluate learning. Why would you complete the course with a quiz compared to a practical assignment. I also do not understand why the slides are not available.
KATONA N P –
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!
KATONA N P –
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!
Aniket G –
Superb crash course for quickly getting up to speed!
Aniket G –
Superb crash course for quickly getting up to speed!
Yahia E G –
Great course. I have learned a lot. I just wish to have more programming exercises to cement our knowledge.
Yahia E G –
Great course. I have learned a lot. I just wish to have more programming exercises to cement our knowledge.
Mario M –
Great introduction. Immediately used new knowledge in current job (marketing data scientist). Recommended course to co workers.
Mario M –
Great introduction. Immediately used new knowledge in current job (marketing data scientist). Recommended course to co workers.
Ayush T –
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.
Ayush T –
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.
Gautam B –
Great intro and overview of the details of Causal Inference methods
Alessandro C –
Very clear, it give good intuition also for technical points.