Causal Analysis: Data Analysis for Social Sciences
Year 3, Term 2 (0.5 credits)
Overview
The course’s main objective is to provide students with an introduction to
causal analysis. This will empower students to critically consume cutting edge
research and will also instill a critical attitude towards weakly identified
research designs. To do so it is paramount to understand the assumptions of
research designs and models.
1. Introduction to causal inference
• Correlation and causation
• Experiments and causality
2. Potential outcomes
• Neyman-Rubin causal model
• Observation studies
3. Natural experiments and field experiments
• Randomized treatments
• As-if random treatments
4. Regression discontinuity design
• Exploiting arbitrary thresholds
• Local average treatment effects
5. Instrumental variables
• Overcoming endogeneity
• Exclusions restriction
• Weak instruments
6. Differences-in-differences
• Finding two twins and only treating one
7. Matching
• Creating balance on observable variables. Show relationship with
regression model.
8. Synthetic controls
• Creating a counterfactual from untreated cases
9. Revision and class project
10. Revision and class project