Causal Analysis with Observational Data

About

Workshop Contents and Objectives

Does smoking cause bad health? Does income inequality increase political extremism? Do schools increase inequality? Many questions of interest to social scientists are causal. This course provides an introduction to modern methods of causal inference using observational data. Building on the potential outcomes framework to causality the course discusses natural experiments, instrumental variables, difference-in-differences (DID), different types of fixed effects models, and regression discontinuity designs (RDD). All these methods allow researchers to control for unobserved variables and therefore to identify causal effects using observational data. The course also provides an introduction to Directed Acyclic Graphs (DAG), which allows us to graphically depict causal relationships.

Workshop design

The course provides both a sound understanding of each method as well as practical exercises to implement these methods using R and Stata. In addition, we discuss exemplary studies implementing each method in the afternoon of each day.

There will also be plenty of time to discuss research projects and ideas related to the methods of the course by the participants. Participants are very much encouraged to apply the methods taught in the course to their own research questions.

Detailed lecture plan (daily schedule)

Day 1: The counterfactual approach to causality and Directed Acyclic Graphs (DAGs)

Day 2: Fixed effects models

Day 3: Difference-in-differences (DiD)

Day 4: Instrumental variables (IV)

Day 5: Regression discontinuity design (RDD)

Class materials

All course material (lecture slides, example code, and exercises, exemplary studies) are provided to participants about a month in advance of the course.

Course leader

Michael Grätz is a SNSF professor in sociology at the University of Lausanne. He is also an associate professor (docent) at the Swedish Institute for Social Research (SOFI), Stockholm University.

Target group

graduate student, doctoral researchers, early career researchers, experienced researchers

Prerequisites

Some elementary knowledge of regression analysis, in particular linear regression, will be necessary to be able to fully follow the content of the course. Statistical analyses will be conducted with R and Stata. A general knowledge of one of these languages will be necessary to implement the practical exercises, as there won’t be the time to learn basic commands. Participants can conduct all exercises in R or Stata, according to their own preferences.

Fee info