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返回到 A Crash Course in Causality: Inferring Causal Effects from Observational Data

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
170 個評分
57 個審閱

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.

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26 - A Crash Course in Causality: Inferring Causal Effects from Observational Data 的 50 個評論(共 57 個)

創建者 DR A N

Aug 22, 2017

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

創建者 Joon-Ku I

Oct 24, 2017

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

創建者 Andrew

May 16, 2018

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

創建者 Xisco B

May 05, 2019

Very interesting studies.

創建者 Leihua Y

May 12, 2019

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.

創建者 张采蔚

Aug 04, 2019

This course is very suitable for beginners, clear and easy to understand.

創建者 Vikram M

May 30, 2019

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

創建者 olufemi B o

Aug 23, 2019

The course itremendoulsy straightened my knowledge of causal evaluation

創建者 Ted L

Aug 24, 2019

Well structured to provide solid understanding of fundamentals, good intuition, and a basic view of applying the covered material.

創建者 Michael S

Jul 08, 2019

Awesome!!! Looking forward to the next one!!!

創建者 Benjamin R

Sep 01, 2019

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.

創建者 Stephen M D

Sep 04, 2019

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).

創建者 Luca A

Sep 24, 2019

A clear and straight-to-the-point introduction to causality. I'm really enjoying the course!

創建者 Marriane M

Oct 08, 2019

Very practical for beginners in causal inference

創建者 Alejandro A P

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.

創建者 Naiqiao H

Feb 27, 2019

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

創建者 Christopher R

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.

創建者 Wayne L

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.

創建者 Michael N

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).

創建者 Patrick W D

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.

創建者 Chris C

Aug 29, 2018

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

創建者 Manuel F

Oct 21, 2018

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

Thanks!

創建者 Joe v D

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.

創建者 Manuel A V S

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.

創建者 Theo B

Jul 02, 2017

In the beginning the course to me was quite difficult, as it has a different perspective on statistics I was used to. Most people tend to say: "correlation is not causality". When it came to propensity scores, matching and so on the possibilities became more clear to me to apply these methods in practice. The pace of the videos is slow, so I played the videos in 1.5 of the time. What I missed was the ability to download the slides. The instructor would look into this, but we're still waiting several weeks later. Another thing I missed was any sense how many other students were in the course.