Module overview

Social scientists constantly make or evaluate arguments about institutions, public policies, laws and individual behaviours. Such arguments depend on underlying facts. “Democratic institutions lead to economic development”. Gun control reduces crime.” “Raising the minimum wage increases unemployment.” “Politicians benefit financially from office”. “Social media increase political polarization’.

How do we know whether these claims are true? In addition to sound theoretical arguments, rigours empirical analysis is a powerful way to get at such facts. This module offers an accessible introduction to the topic of causal inference in quantitative analysis and its practice.

The module strives to minimize technical notation by providing a largely nontechnical overview of the newest methods for causal inference along with practical guidelines for designing and implementing research projects aimed at establishing causal relationships. These techniques are not only used by national governments and international organizations to set and track targets, but they are increasingly applied by managers in the private sector to determine budget allocations and guide decisions.

Module aims

The aim of this module is to provide an accessible introduction to the topic of causal inference in social science. Students will learn the newest empirical techniques to study cause-and-effect relationships regarding real world events and discover the pitfalls when working with data. The statistical concepts are illustrated using data and examples primarily from the fields of political science, but also from law, economics and sociology.

1. Determining Which Method to Use for a Given Question
2. How to Produce a Policy Research Report

Learning outcomes

• Assess the quality of published research with the aim of showing how the process of knowledge creation through research does or does not lead to clear conclusions regarding causal effects
• Critically evaluate how research is presented in the public domain (e.g., media) to be a better consumer of reported findings
• Learn the five basic empirical techniques - random assignment, regression, instrumental variables, regression discontinuity, and differences-in-differences – using Stata
• Conduct meaningful and creative empirical work that investigates causal relationships