Вернуться к A Crash Course in Causality: Inferring Causal Effects from Observational Data

4.7

Оценки: 169

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Рецензии: 57

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!...

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.

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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автор: 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.

автор: 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.

автор: 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.

автор: Rudy M P

•Apr 17, 2018

I learned the basics of causality inference and want even more now!

автор: Pichaya T

•Feb 26, 2018

Excellent courses. I gain my expectations.

автор: 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!

автор: 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.

автор: Takahiro I

•Sep 26, 2017

The best lecture series of causality

автор: DR A N

•Aug 22, 2017

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

автор: 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.

автор: Michael L

•Nov 26, 2017

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

автор: Chang L

•Sep 11, 2017

enjoyed it very much

автор: Vikram R

•Mar 14, 2018

Great course for getting your hands dirty with some real causal methods.

автор: 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.

автор: Bob H

•Oct 20, 2017

Good intro of the techniques.

автор: 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.

автор: Joon-Ku I

•Oct 24, 2017

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

автор: Akash G

•Jun 17, 2018

Amazing Course! Really Helpful. I would love to have a similar full-duration course :D

автор: Min-hyung K

•Jul 01, 2017

Thanks so much for providing this great lecture.

автор: 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.

автор: Vlad V

•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.

автор: 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.

автор: Andrew

•May 16, 2018

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

автор: 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.

автор: Arka B

•May 31, 2018

gives thorough basic intro to causal inference

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