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.

## 51 - A Crash Course in Causality: Inferring Causal Effects from Observational Data 的 75 个评论（共 148 个）

Aug 24, 2019

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

Mar 11, 2022

Covered from mathematical concepts to practical statistical analysis with R. A perfect course for newcomers on causal inference.

Jan 12, 2020

Great introduction. Immediately used new knowledge in current job (marketing data scientist). Recommended course to co-workers.

Oct 24, 2017

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

Dec 28, 2020

Excellent course. This course helped me to develop my intuition on some of the more abstract concepts in causality.

Aug 31, 2017

Not only good for bio stats, it has also profound impact to my understanding of a/b testing in the internet world.

Aug 11, 2017

This is an excellent course taught by a very competent professor in a very simple to understand and intuitive way.

Dec 20, 2020

Excellent course, extremely well presented that helps clarify a lot of statistical concepts in an intuitive way.

Jul 3, 2021

Good course to review key techniques in causal inference. Would be nice to have more in-depth course in sequel.

Nov 26, 2017

Excellent overview on causality inference and handling confounders combined with practical examples and R code.

Jul 25, 2022

Good explanation and hands-on R practice.

Highly recommended for those working on the observational studies

Aug 22, 2017

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

Jan 18, 2021

Very enlightening. Well explained, and strikes a great balance between theory & practical aspects.

Dec 28, 2020

This course is actually great. It is a basic course but it does not mean it is for an amateur.

May 1, 2020

The best course on causal inference on Coursera. Lots of examples, easy to follow materials.

Sep 24, 2019

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

Apr 5, 2019

Good course on the over view of Causality. Not too technical, but not too light and fluffy.

Oct 10, 2021

Great course, nice balance between statistical theory and practical application using R

Jan 25, 2021

Extremely helpful for people who just started to do research on observational studies!

Jun 17, 2018

Amazing Course! Really Helpful. I would love to have a similar full-duration course :D

Dec 28, 2020

Great course. It is good for broad set of people with different level of math skill.

Dec 27, 2020

Great course for getting good intuitions on central concepts in causal inference

Apr 9, 2021

excelent!!!, this is a great course. The teacher is really good explaining.

Apr 8, 2021

Great intro to causality with great examples and sample R code. Thank you!

Apr 6, 2021

Detailed explanations about the rationale and statistical methods to use.