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Essential Causal Inference Techniques for Data Science に戻る

Coursera Project Network による Essential Causal Inference Techniques for Data Science の受講者のレビューおよびフィードバック

4.6
25件の評価
4件のレビュー

コースについて

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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Essential Causal Inference Techniques for Data Science: 1 - 4 / 4 レビュー

by Keerat K G

2021年1月31日

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

by 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

by Chiara L

2022年3月10日

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

by seyed r m

2022年2月3日

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.