Causal Inference: The Mixtape

Causal Inference: The Mixtape

by Scott Cunningham

Paperback(New Edition)

View All Available Formats & Editions
Choose Expedited Shipping at checkout for delivery by Thursday, December 2


An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences
“Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC)

Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.

Related collections and offers

Product Details

ISBN-13: 9780300251685
Publisher: Yale University Press
Publication date: 01/26/2021
Edition description: New Edition
Pages: 584
Sales rank: 261,402
Product dimensions: 5.50(w) x 8.50(h) x 1.16(d)

About the Author

Scott Cunningham is professor of economics at Baylor University. He is also coeditor of The Oxford Handbook of the Economics of Prostitution.

Table of Contents

Acknowledgments ix

Introduction 1

What Is Causal Inference? 3

Do Not Confuse Correlation with Causality 6

Optimization Makes Everything Endogenous 8

Example: Identifying Price Elasticity of Demand 10

Conclusion 14

Probability and Regression Review 16

Directed Acyclic Graphs 96

Introduction to DAG Notation 97

Potential Outcomes Causal Model 119

Physical Randomization 123

Randomization Inference 148

Conclusion 174

Matching and Subclassification 175

Subclassification 175

Exact Matching 191

Approximate Matching 198

Regression Discontinuity 241

Huge Popularity of Regression Discontinuity 241

Estimation Using an RDD 252

Challenges to Identification 282

Replicating a Popular Design: The Close Election 289

Regression Kink Design 312

Conclusion 313

Instrumental Variables 315

History of Instrumental Variables: Father and Son 315

Intuition of Instrumental Variables 319

Homogeneous Treatment Effects 323

Parental Methamphetamine Abuse and Foster Care 329

The Problem of Weak Instruments 337

Heterogeneous Treatment Effects 346

Applications 352

Popular IV Designs 359

Conclusion 384

Panel Data 386

DAG Example 386

Estimation 388

Data Exercise: Survey of Adult Service Providers 396

Conclusion 405

Difference-in-Differences 406

John Snow's Cholera Hypothesis 406

Estimation 411

Inference 423

Providing Evidence for Parallel Trends Through Event Studies and Parallel Leads 425

The Importance of Placebos in DD 433

Twoway Fixed Effects with Differential Timing 461

Conclusion 509

Synthetic Control 511

Introducing the Comparative Case Study 511

Prison Construction and Black Male Incarceration 525

Conclusion 540

Bibliography 541

Permissions 555

Index 561

Customer Reviews