4.7
449 个评分
146 条评论

课程概述

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!...

热门审阅

WJ

Sep 11, 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

Dec 27, 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.

26 - A Crash Course in Causality: Inferring Causal Effects from Observational Data 的 50 个评论（共 148 个）

Oct 10, 2021

I expected more from this course. It gets too deep into the more advanced topics without using specific examples to showcase the main ideas. The instructor could also be more engaging, I had to watch the videos at x1.25 to be able to keep my attention on them

Apr 1, 2021

I am sure the instructor is very knowledgeable and excellent in front of a class. His style does not work online.

Sep 30, 2021

C​oming from an economics background, I really enjoyed seeing how causal inference is being approached in a different field. While the methods used are generally the same, the motivation of these methods or the focus on certain tools and aspects sometimes appears to differ. That really gave me a new perspective on some of the methods in my causal inference toolkit. Good course!

May 21, 2022

This course helped me secure a beachhead in the realm of Causal Inference. My background is in computer science and machine learning. I was struggling with all the terms used in Causal Inference. It is a fascinating topic and this course provides well connected, solid explanations of terms, theory and its application using R. Thank you.

Jul 27, 2021

This course is excellent. The quiz helps to make sure you get the key assumptions and method ideas right, while the programming exercises ensure that you know how each method works and how they can be implemented either manually or by using some of the available statistical R packages for causal effect estimation.

Aug 26, 2021

This course does a fantastic job of balancing the theoretical and practical aspects of causal inference. Additionally, it takes the student through three very different techniques of causal inference that apply to common real-world situations in a relatively short course.

Apr 16, 2022

This is an excellent course. I audited the because I wanted to learn more about marching and prospensity score and it was awesome. The explanation is quite easy to understand. I would recommend the course to anyone who wants to learn casual inference.

Enjoy

Apr 14, 2020

This course is a short one, but power-packed. It gives a different dimension of understanding the data, it's linkages and further extrapolations. Each word of Jason has to be heard properly as he continues to explain facts in a very lucid manner.

Nov 21, 2021

This is a very good course to take if you want to get important causal inference methods concepts. Even though it has some math concepts, the Professor does a good job of introducing them really well for a beginner. I would strongly recommend!

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

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.

Jan 20, 2021

It is a very useful course that combines conceptual and technical aspects of Applied Causal Inference.

The presentations are very clear, the Examples and Exercises (R-coded) have been very useful for me to practice specific R-packages.

May 16, 2020

Great course, especially if you are reasonably familiar with R and basic stats and interested in approaching causal analysis. Word of caution: If you have never used R, you will have trouble getting through some of the assignment.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!

Feb 21, 2021

Really wonderful course--I learned so much in the way of theory and practical application in R. Some links need to be updated and it would be best to provide students with answers to worked examples for the quiz questions.

Sep 12, 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.

Apr 9, 2021

The course is very simply explained, definitely a great introduction to the subject. There are some missing links, but minor compared to overall usefulness of the course.

Feb 21, 2021

A great start for those starting to explore causal inference. The somewhat dry delivery of the lectures is fully compensated by how clear and informative they are.

Apr 4, 2021

My work involves working with observational data. This course taught me to think in more formal and organized way on topics and questions of causal inference.

Feb 18, 2022

Great introduction to the field covering model synthesis of causality ideals. Glitches in assignments - make sure to check the discussion for workarounds.

Mar 12, 2021

Excellent video lectures. Challenging end of module quizzes. I found more challenging doing the practical exercises because I had no experience with R.

Dec 15, 2021

A very thorough and pleasant intro into the topic. Thanks from Russia! To the lecturer - be more confident in yourself! You are great at your stuff :)

Nov 28, 2021

G​reat course! I am glad i came accross it. Helped me a great deal with my project at work. I wish there were more courses by this professor.

May 15, 2018

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

May 24, 2021

Очень лаконичный и полезный курс. Очень помог разобраться в теме Causal Inference. Отлично подходит для начала вхождения в данную тему.