Regression Discontinuity Design and Instrumental Variables
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.
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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- 1Learn about regression discontinuity design and instrumental variables for causal inference.
— 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.
Mimic experiments with the data you already have and measure the effects of treatment even with incomplete data.
DetailsEarn a certificate of completion
2 hours to complete in total
Learn about regression discontinuity design and instrumental variables for causal inference.