Information détaillée concernant le cours
Titre | Causal Inference |
Dates | 17-18 March 2025 |
Lang |
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Organisateur(s)/trice(s) | Dre Elisa Volpi, UNIGE |
Intervenant-e-s | Prof. Florian Foos, London School of Economics and Political Science |
Description | 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). |
Programme | 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 |
Lieu |
Genève |
Information | |
Places | 15 |
Délai d'inscription | 10.03.2025 |

