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A Crash Course in Causality: Inferring Causal Effects from Observational Data に戻る

ペンシルベニア大学(University of Pennsylvania) による A Crash Course in Causality: Inferring Causal Effects from Observational Data の受講者のレビューおよびフィードバック

4.7
449件の評価

コースについて

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!...

人気のレビュー

WJ

2021年9月11日

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.

MF

2017年12月27日

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.

フィルター:

A Crash Course in Causality: Inferring Causal Effects from Observational Data: 126 - 148 / 148 レビュー

by Christopher R

2019年2月10日

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.

by Ruixuan Z

2019年6月22日

Some of the materials are bit academical and away from industry, however, I found most of the materials relevant and practical.

by Alvaro F

2020年8月25日

Great course, the title is exactly what you will get: the basics on inferring causal effects from observational data

by Yahia E

2020年1月9日

Great course. I have learned a lot. I just wish to have more programming exercises to cement our knowledge.

by Jeesoo J

2021年1月25日

The course is very helpful for beginners to understand. Also, to be able to practice through R is helpful.

by Chris C

2018年8月28日

Could use a bit more guidance on the projects, but overall a helpful course. Gets straight to the point.

by Manuel F

2018年10月21日

Interesting introductory course about causality. Good "compilation" in just 5 weeks.

Thanks!

by Naiqiao H

2019年2月27日

The course is very useful for beginners. The materials are clear and easy to understand.

by Lorena L

2021年5月2日

I really enjoyed this course and I appreciated the practice exercise in R.

by Fernando C

2017年11月24日

They could offer more applied exercises in R. But, it was also great.

by Lyons B

2020年9月20日

The lectures are good, and they might consider covering more topics.

by Gavin M

2020年12月4日

It was well laid out, and overall helpful.

by Javed A

2020年11月27日

A good course. Bit difficult for novices.

by Juan C

2019年10月7日

Great

by Andrew L

2019年11月28日

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.

by Robert S

2021年12月17日

I​ think it would be nice to have a bit of an overview how the methods compare to others in the field of causal inference. Also the slides could contain more illustrations. However, I liked the selection of the material.

by Enrique O M

2021年9月4日

Good content. But irregular assignments, most with no feedback. Moreover some exercises could have errors, or at least ambiguous enunciates.

by Kasra S

2021年8月14日

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

by Ignacio S R

2018年4月30日

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

by Francisco P

2019年5月30日

Hard to understand

by Scott M

2022年2月16日

The course material is excellent, but the course description dramatically under-estimates the study time needed to complete the course. This is especially true for the R assignments if you are not already *very* comfortable in R. There are also many problems with link rot and software/version compatibility issues for the R exercises.

I would have given the course 4 stars were it not for the unforgiving nature of the R exercises.

Overall, I would recommend this course for someone if they are already quite comfortable in R, or are willing to pout in at least 20 hours of work for each of the R assugnments.

by Siyu H

2021年2月14日

This is a very theoretical course with much math formula and less well-explained practical examples to better illustrate those formula. I came to this course hoping to learn about new ideas and techniques of experiment design for causal effect when randomized experiments are not possible. Unfortunately I did not achieve this goal. This is just my personal view. If you come with a different purpose, you might find this course more useful than I did.

by Eva Y G

2019年9月28日

Can not download slides which make the source material very inaccessible