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.
Essential Causal Inference Techniques for Data Science
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Essential Causal Inference Techniques for Data Science
Instructor: Vinod Bakthavachalam
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4.5
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Beginner level
No prior experience required
2 hours
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Hands-on learning
What you'll learn
Learn the limitations of AB testing and why causal inference techniques can be powerful.
Understand the intuition behind and how to implement the four main causal inference techniques in R.
Explore newer methods at the intersection of causal inference and machine learning and implement them in R.
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About this Guided Project
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In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Use Controlled / Fixed Effects Regression to estimate impact of customer satisfaction on customer revenue.
Use Regression Discontinuity to estimate the impact of customer support on renewal probability.
Use Difference in Difference to estimate the impact of raising prices on revenue.
Use Instrumental Variables to see whether using the mobile app leads to increased customer retention.
Use Double Selection to speed up AB tests and get more precise estimates.
Use Causal Forests to find heterogeneous treatment effects separated by registration source for impact of discounts.
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Reviewed on Jan 30, 2021
Decent start to Causal Inference Techniques with sufficient theory for a project.
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