# 学生对 宾夕法尼亚大学 提供的 A Crash Course in Causality: Inferring Causal Effects from Observational Data 的评价和反馈

4.7
415 个评分
138 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.

## 76 - A Crash Course in Causality: Inferring Causal Effects from Observational Data 的 100 个评论（共 139 个）

Apr 17, 2018

I learned the basics of causality inference and want even more now!

Mar 31, 2020

Very clear, it give good intuition also for technical points.

Sep 1, 2020

great course and practical introduction to causal inference.

Jul 27, 2020

A good course with detailed explanation and data examples

Sep 4, 2020

Excellent course in causal effect estimation. Thanks .

Dec 15, 2019

Superb crash course for quickly getting up to speed!

Oct 8, 2019

Very practical for beginners in causal inference

Jul 1, 2017

Thanks so much for providing this great lecture.

May 31, 2018

gives thorough basic intro to causal inference

Jul 7, 2019

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

Sep 8, 2020

Detailed and excellent course on causality

Feb 26, 2018

Excellent courses. I gain my expectations.

Jan 3, 2021

excellent course, very very useful!!

Sep 26, 2017

The best lecture series of causality

Aug 28, 2018

no nonsense, in depth and practical

May 18, 2021

A very good introduction course.

Aug 2, 2020

intense and well crafted course!

Apr 3, 2020

wonderful course, very helpful

Oct 19, 2017

Good intro of the techniques.

Dec 21, 2020

Jason Roy! He is a monster!

May 5, 2019

Very interesting studies.

Aug 29, 2020

Very well presented.

Sep 11, 2017

enjoyed it very much

Feb 22, 2020

Enlightening.

Jun 4, 2021

w​onderful!