Causal Inference

17-18 March 2025

Dre Elisa Volpi, UNIGE

Prof. Florian Foos, London School of Economics and Political Science

Information: “Correlation is not causation”. You must have heard this warning many times. But, what then

is causation, and how can we identify the causal effect of political messages, policies or programmes? This

module introduces students to the ideal of the randomized experiment, and other common methods of causal

inference using quasi-experimental data.

Aims: The module provides an introduction to the design-based approach to causal inference. Instead of

using fancy modelling approaches to correct post-hoc for potential biases, the module encourages students to

think about challenges to causal inference at the design stage of a study. Published work will be evaluated

based on how it addresses three key assumptions underlying causal inference: independence, excludability, and

non-interference. The module will cover identification strategies for a variety of causal research questions in

Political Science. After introducing participants to the design-based approach to causal inference in general, the

module will cover the Regression Discontinuity Design, the Difference-in-Differences Design and the Synthetic

Control Method, three methods that allow design-based inference with quasi-experimental data, in detail. The

module is taught as a combination of lectures and applied computer labs in R (Stata code will be provided).

Prerequisites: The only pre-requisite is any course covering linear regression. There is relatively little assumed

knowledge, and the aim is to build the statistical foundations from the ground up. If you have conducted a

hypothesis test of any kind, you probably have the requisite skills.

Learning Outcomes: Participants will understand the potential outcomes framework, and the key assump-

tions underlying causal inference, and will be able to choose appropriate methods for a variety of research

questions posing different identification challenges. Moreover, they will gain the practical skills of applying

these insights and the statistical knowledge to their own research ideas.

Course Dropbox: Some readings and code files are available from the course dropbox.

Required textbook

Cunningham, Scott. Causal Inference: The Mixtape, Yale University Press, 2020.

Recommended textbooks

Angrist, Joshua and Joern-Steffen Pischke. Mostly Harmless Econometrics: An Empiricist’s Companion,

Princeton: Princeton University Press, 2009.

Bueno de Mesquita, Eitan, and Anthony Fowler. Thinking Clearly in a Data-Driven Age, 2019: available via

course dropbox.

Dunning, Thad. Natural Experiments in the Social Sciences. A Design-Based Approach, Cambridge: Cambridge

University Press, 2012.

Gerber, Alan and Donald P. Green. Field Experiments: Design, Analysis, and Interpretation, New York: W.W.

Norton, 2012.

Imbens, Guido W. and Donald B. Rubin. Causal Inference for Statistics, Social and Biomedical Sciences: An

Introduction. Princeton: Princeton University Press, 2015.

Software: Participants will have a choice between using R (demonstration in class) and Stata (code will be

provided).

Workshop Outline

1. Introduction to causal inference

2. Potential Outcomes Framework and ATE

3. Regression Discontinuity Design (RDD): Foundations

4. Regression Discontinuity Design (RDD): Application

5. Difference-in-Differences Design (DiD): Foundations

6. Difference-in-Differences Design (DiD): Application

7. Synthetic Control Method (SCM): Foundations

8. Synthetic Control Method (SCM): Application

9. Advice on students’ research designs

Genève

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