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学生对 Coursera Project Network 提供的 Essential Causal Inference Techniques for Data Science 的评价和反馈

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38 个评分

课程概述

Data scientists often get asked questions related to causality: (1) did recent PR coverage drive sign-ups, (2) does customer support increase sales, or (3) did improving the recommendation model drive revenue? Supporting company stakeholders requires every data scientist to learn techniques that can answer questions like these, which are centered around issues of causality and are solved with causal inference. In this project, you will learn the high level theory and intuition behind the four main causal inference techniques of controlled regression, regression discontinuity, difference in difference, and instrumental variables as well as some techniques at the intersection of machine learning and causal inference that are useful in data science called double selection and causal forests. These will help you rigorously answer questions like those above and become a better data scientist!...

热门审阅

CK

Apr 2, 2025

Instructor is very knowledgeable. Best explanations I've come across for causal inference principles. The labs in R are great and have a "real world" feel to them.

KG

Jan 30, 2021

Decent start to Causal Inference Techniques with sufficient theory for a project.

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1 - Essential Causal Inference Techniques for Data Science 的 9 个评论（共 9 个）

创建者 Tom B

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Apr 16, 2021

it's a neat format, but there's not a huge amount of material in the course, unless you can keep the code. A lot of these models would be better as glms not linear models, but that isn't really discussed. it would also be useful to see more on the causal forest, which is the area which interested me in particular

创建者 Keerat K G

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Jan 31, 2021

Decent start to Causal Inference Techniques with sufficient theory for a project.

创建者 Cameron D K

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Apr 3, 2025

Instructor is very knowledgeable. Best explanations I've come across for causal inference principles. The labs in R are great and have a "real world" feel to them.

创建者 Chiara L

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Mar 10, 2022

For someone who's unfamiliar with R and causal inference, this helped a lot with familiarizing but it's too short to go fully in-depth. Would like to have discussed more practical ways to apply these methods to machine learning and when-to-use-which technique

创建者 Jonas R M

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Mar 17, 2025

Great course and hands-on. A bit too fast with the ML part, should've taken more time to explain. Other than that, fun!

创建者 Jiaxing S

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Apr 18, 2025

I wish it was on Python

创建者 Sasmito Y H

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Sep 19, 2022

Delivering the promised essential with adequate value.

创建者 Nersu A

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Aug 19, 2022

no reading material and can't revise the concepts

创建者 seyed r m

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Feb 3, 2022

Good match between lecture/example and tests. It would be better if there were more real world examples and the course included use of applying Causal Inference to time-series data.