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Causal Inference with R - Instrumental Variables & RDD

Updated 03/2025

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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).

Prerequisites

There are no prerequisites for this course

1

Introduction to Instrumental Variables

Course Trailer for Causal Inference with R – Instrumental Variables & RDD

The Logic of Instrumental Variables

Visual Logic of Instrumental Variables

What is the Relationship Between the Instrument and the Outcome?

What is the Relationship Between the Instrument and the Treatment?

What is the Relationship Between the Instrument and the Other Pre-Treatment Variables?

How Do We Calculate a Causal Effect with Instrumental Variables Analysis?

Some Instrumental Variables Terminology

Is it Endogenous vs Exogenous Variation?

Instrumental Variables in Action: Education and Wages (graphs)

Instrumental Variables in Action: Education and Wages (tables)

What if Our Data Shows No Clear Correlation Between Treatment and Outcome?

The Three Instrumental Variables Assumptions

Alan the Egg Farmer Turns to Instrumental Variables

2

Instrumental Variables in Practice

The Problem of Weak Instruments

An Example of a Weak Instrument

Overview of Research on Property Rights & Economic Development

Where Do Instruments Come From?

Using IV When There is Measurement Error

Logical Arguments About Instrumental Variables

Let's Code: Practice Using Instrumental Variables

Practice Using Instrumental Variables: CreditCo

Refutability and Nonrefutability of the Instrumental Variables Assumptions

Questioning the Validity of Instrumental Variables Results

Indirect Inference

Let's Code: Practice Computing Causal Effects Using Indirect Inference

Practice Computing Causal Effects Using Indirect Inference: CreditCo

Two Stage Least Squares (2SLS)

Overview of Squatters & Property Rights Experiment

Example of IV: Property Rights & Market Beliefs

Let's Code: Practice Computing Causal Effects Using 2SLS

Practice Computing Causal Effects Using Two-Stage Least Squares (2SLS): CreditCo

Comparing the Estimates from Indirect Inference and 2SLS

Solving Noncompliance in Experiments with Instrumental Variables

Overview of the Oregon Healthcare Experiment

Using IV to Solve Noncompliance in the Oregon Heathcare Insurance Experiment

Balancing Assumptions and Data When Using IV

Let's Code: Practice Using IV to Solve Noncompliance in the OHIE

Practice Using IV to Solve Noncompliance in the OHIE

3

Regression Discontinuity Design

Regression Discontinuity: Looking at People on the Edge

Regression Discontinuity: More Analysis of Thistlethwaite and Campbell

Key Variables in an RD Design

Identifying Key Variables in an RD Design

How to Compute Causal Effects in a Regression Discontinuity Analysis

Visualizing RD Designs

Let's Code: Examining Manipulation in Regression Discontinuity Design

Examining Manipulation in Regression Discontinuity Design

Using RDD to Study Neighborhoods and Schools

Thinking about confounders in an RD design

Using Geographical Borders More Generally in RD Designs

Let's Code: Practice Computing Regression Discontinuity Effects

Practice Computing Regression Discontinuity Effects

"Fuzzy" Regression Discontinuity: Addressing Blurry Lines Between Groups

Identifying Fuzzy and Sharp RDDs

Fuzzy RDD and Swiss Religion: Checking the Numbers

4

The Local Average Treatment Effect of IV

Which Causal Effects Are We Actually Getting with Instrumental Variables?

Defining LATE: The Local Average Treatment Effect

LATE and the ATE for Always-Takers

The "No Defiers" Assumption

Computing LATE, Part 1: Dividing the Population

Computing LATE, Part 2: Finding the Compliers

Computing LATE, Part 3: Getting a Result

The Pros & Cons of LATE

Interpreting Different Results when Working with Noncompliance

ATEs, CATEs, and LATEs: What's the Difference?

An Alternate Way to LATE?

Causal Inference with R - Instrumental Variables & RDD

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