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Causal Inference with R - Instrumental Variables & RDD

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Welcome to the 5th course in our series on causal inference concepts and methods created by Duke University with support from eBay, Inc. Designed to teach you causal inference concepts, methods, and how to code in R with realistic data, this course focuses on how to use the advanced methods of instrumental variables and regression discontinuity to find causal effects. We will give you the reasoning be behind the methods, how you need to argue their validity, and what they look like in practice. We’ll stay away from dense statistical math and focus instead on higher level concepts that data scientists need to always consider when examining and making inferences about data. The course instructors and creators are Dr. Matt Masten (Duke University), James Speckart (Duke), Tyler Ransom (University of Oklahoma), Atilla Gyetvai (Duke) and Alexandra Cooper (Duke).

Introduction to Instrumental Variables

This chapter will introduce you to Instrumental Variables (IV) analysis, a tricky but powerful method to find causality through indirect inference

Instrumental Variables in Practice

This chapter covers more details about Instrumental Variables Analysis, and allows you to practice instrumental variables for yourself

Regression Discontinuity Design

This chapter introduces you to Regression Discontinuity Design (RDD), which is often considered a more flexible relative of Instrumental Variables

The Local Average Treatment Effect of IV

This chapter explains the treatment effect we are estimating with the instrumental variables method - LATE, or the Local Average Treatment Effect