Regression Discontinuity Design and Instrumental Variables

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Why Regression Discontinuity Design and Instrumental Variables?

A goal of many analytics projects is to answer “how much did factor x affect the outcome?”, otherwise known as the treatment effect. But it can be very hard to do that with real data because it’s often incomplete. This course will introduce you to two techniques to fix that problem: Regression Discontinuity Design and Instrumental Variables.

Take-Away Skills

In this course, you will learn how and when to apply Regression Discontinuity Design (RDD) and Instrumental Variables. You’ll be able to make the most of whatever data you have by mimicking experiments. You’ll learn how and why cutoff points are so valuable in real-world data and how to get around assumptions like conditional exchangeability (defined in the course) with estimation techniques.

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    Learn about regression discontinuity design and instrumental variables for causal inference.

What you'll create

Portfolio projects that showcase your new skills

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How you'll master it

Stress-test your knowledge with quizzes that help commit syntax to memory

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testimonial

— Madelyn, Pinterest

I know from first-hand experience that you can go in knowing zero, nothing, and just get a grasp on everything as you go and start building right away.

Course Description

Mimic experiments with the data you already have and measure the effects of treatment even with incomplete data.

Details

Earn a certificate of completion
2 hours to complete in total
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Learn about regression discontinuity design and instrumental variables for causal inference.