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# Causal Inference with R - Instrumental Variables & RDD

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## Course Description

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).

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1. 1

### Introduction to Instrumental Variables

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

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Course Trailer for Causal Inference with R – Instrumental Variables & RDD
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The Logic of Instrumental Variables
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Visual Logic of Instrumental Variables
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What is the Relationship Between the Instrument and the Outcome?
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What is the Relationship Between the Instrument and the Treatment?
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What is the Relationship Between the Instrument and the Other Pre-Treatment Variables?
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How Do We Calculate a Causal Effect with Instrumental Variables Analysis?
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Some Instrumental Variables Terminology
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Is it Endogenous vs Exogenous Variation?
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Instrumental Variables in Action: Education and Wages (graphs)
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Instrumental Variables in Action: Education and Wages (tables)
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What if Our Data Shows No Clear Correlation Between Treatment and Outcome?
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The Three Instrumental Variables Assumptions
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Alan the Egg Farmer Turns to Instrumental Variables
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2. 4

### The Local Average Treatment Effect of IV

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

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Matt Masten

I'm an econometrician working on identification and causal inference.
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SSRI @ Duke University

We create modular online educational content for Duke University's Social Science Research Institute to share with students and professionals around the world. If you have any feedback for us about any part of this course, please let us know!
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Alexandra Cooper

Alexandra Cooper earned a Ph.D. in political science from the University of North Carolina at Chapel Hill and has taught at Duke, UNC, UNC-Charlotte, and most recently, at Lafayette College, before joining Duke University to plan and develop the Social Science Research Institute (SSRI). Initially responsible for SSRI's day-to-day programming and operations, as the institute has grown and added staff, she has had the opportunity to focus on coordinating educational programming. Her key responsibilities include organizing workshops and events, and supporting programming focused on improving knowledge and applications of the tools and methods of the social and behavioral sciences.
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