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## Comentários e feedback de alunos de A Crash Course in Causality: Inferring Causal Effects from Observational Data da instituição Universidade da Pensilvânia

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## Sobre o curso

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!...

## Melhores avaliações

WJ

11 de set de 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.

MF

27 de dez de 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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## 126 — 148 de 148 Avaliações para o A Crash Course in Causality: Inferring Causal Effects from Observational Data

por Christopher R

10 de fev de 2019

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.

por Ruixuan Z

22 de jun de 2019

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

por Alvaro F

25 de ago de 2020

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

por Yahia E

9 de jan de 2020

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

por Jeesoo J

25 de jan de 2021

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

por Chris C

28 de ago de 2018

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

por Manuel F

21 de out de 2018

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

Thanks!

por Naiqiao H

27 de fev de 2019

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

por Lorena L

2 de mai de 2021

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

por Fernando C

24 de nov de 2017

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

por Lyons B

20 de set de 2020

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

por Gavin M

4 de dez de 2020

It was well laid out, and overall helpful.

por Javed A

27 de nov de 2020

A good course. Bit difficult for novices.

por Juan C

7 de out de 2019

Great

por Andrew L

28 de nov de 2019

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.

por Robert S

17 de dez de 2021

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.

por Enrique O M

4 de set de 2021

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

por Kasra S

14 de ago de 2021

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

por Ignacio S R

30 de abr de 2018

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

por Francisco P

30 de mai de 2019

Hard to understand

por Scott M

16 de fev de 2022

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.

por Siyu H

14 de fev de 2021

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.

por Eva Y G

28 de set de 2019