Explore the concepts of causal inference in survey data, learn some of the underlying theory of causality, and focus on empirical methods to identify causality in data.
Overview
Syllabus
Introduction
- Causality unlocked: A primer for data analysts
- What you can learn
- What you should know
- Why causal inference matters
- The gold standard: Experimental data
- What is different about survey data?
- Observables vs. unobservables causes
- What are treatment effects?
- An applied example: The LaLonde debate
- Setting up a randomized controlled trial
- Analyzing a randomized controlled trial
- Surveys with cross-sectional data
- Regression analysis
- Propensity score matching
- Regression discontinuity designs
- Instrumental variable models
- Surveys with longitudinal data
- Regression models with time effects
- Fixed effects regression models
- Difference-in-difference estimation
- Synthetic control methods
- How to evaluate causal robustness
- How to present causal statistics
- Next steps and additional resources
Taught by
Franz Buscha
