Essential Causal Inference Techniques for Data Science

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In this Guided Project, you will:
2 hours
Beginner
No download needed
Split-screen video
English
Desktop only

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!

Skills you will develop

  • Regression Discontinuity Design

  • Causal Inference

  • Instrumental Variable

  • regression

  • Difference In Differences

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

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In a split-screen video, your instructor guides you step-by-step

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