# 學生對 宾夕法尼亚大学 提供的 A Crash Course in Causality: Inferring Causal Effects from Observational Data 的評價和反饋

4.7
201 個評分
69 條評論

## 課程概述

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!...

## 熱門審閱

##### 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.

## 51 - A Crash Course in Causality: Inferring Causal Effects from Observational Data 的 70 個評論（共 70 個）

Nov 22, 2019

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!

Oct 12, 2019

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.

Aug 25, 2017

Very approachable as someone with a Masters in Statistics, probably tough if you are not comfortable with notation and concepts of intermediate prob/stats. Extremely clear and concise presentation. Coverage of methodology is a little weak, there is not enough discussion of the dangers of doing causal inference on observational data, nor of the dangers of the proposed methods. For instance, propensity score matching is ineffective or even harmful in the face of hidden confounders, which in the real world you almost always have.

May 06, 2018

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.

Dec 09, 2018

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).

Mar 17, 2019

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.

Dec 15, 2018

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.

Jul 15, 2018

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.

Feb 11, 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.

Jun 23, 2019

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

Jan 09, 2020

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

Aug 29, 2018

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

Oct 21, 2018

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

Thanks!

Feb 27, 2019

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

Nov 24, 2017

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

Oct 07, 2019

Great

Nov 28, 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.

Apr 30, 2018

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

May 30, 2019

Hard to understand

Sep 29, 2019