Introduction
What is Causal Inference?
Do Not Confuse Correlation with Causality
Optimization Makes Everything Endogenous
Example : Identifying Price Elasticity of Demand
Probability and Regression Review
Directed Acyclic Graphs
Introduction to DAG Notation
Potential Outcomes Causal Model
Physical Randomization
Randomization Inference
Matching and Subclassification
Subclassification
Exact Matching
Approximate Matching
Regression Discontinuity
Huge Popularity of Regression Discontinuity
Estimation Using an RDD
Challenges to Identification
Replicating a Popular Design : the Close Election
Regression Kink Design
Instrumental Variables
History of Instrumental Variables : Father and Son
Intuition of Instrumental Variables
Homogeneous Treatment Effects
Parental Methamphetamine Abuse and Foster Care
The Problem of Weak Instruments
Heterogeneous Treatment Effects
Applications
Popular IV Designs
Panel Data
DAG Example
Estimation
Data Exercise : Survey of Adult Service Providers
Difference-in-Differences
John Snow's Cholera Hypothesis
Estimation
Inference
Providing Evidence for Parallel Trends Through Event Studies and Parallel Leads
The Importance of Placebos in DD
Twoway Fixed Effects with Differential Timing
Synthetic Control
Introducing the Comparative Case Study
Prison Construction and Black Male Incarceration

Causal inference encompasses the tools that allow social scientists to determine what causes what. 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. Scott Cunningham is professor of economics at Baylor University. He is also coeditor of "The Oxford Handbook of the Economics of Prostitution".