Instructor: Vinod Bakthavachalam
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(35 reviews)
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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Only available on desktop
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.
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
35 reviews
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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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By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
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