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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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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!...

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1 - Essential Causal Inference Techniques for Data Science 的 6 个评论(共 6 个)

创建者 Tom B

Apr 16, 2021

创建者 Keerat K G

Jan 31, 2021

创建者 Chiara L

Mar 10, 2022

创建者 Sasmito Y H

Sep 19, 2022

创建者 Nersu A

Aug 19, 2022

创建者 seyed r m

Feb 3, 2022