返回到 Essential Causal Inference Techniques for Data Science
學生對 Coursera Project Network 提供的 Essential Causal Inference Techniques for Data Science 的評價和反饋
16 個評分
課程概述
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 2 個評論（共 2 個）
創建者 Keerat K G
•2021年1月31日
Decent start to Causal Inference Techniques with sufficient theory for a project.
創建者 Tom B
•2021年4月16日
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
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