Detailed course description:
- Introduction to causal inference, quick history
- Potential outcomes, counterfactuals and basic definitions
- Randomization
- DAG notation
- Propensity score matching, Nearest neighbor covariate matching
- Instrumental variables, 2SLS estimation
- Weak instruments, Just-Identified IV, Overidentification
- Formula IVs
- Weighting Estimators of the Local Average Treatment
- Heterogenous treatment effects
- Regression discontinuity identification
- Optimal bandwidth selection, Robust confidence intervals,
- Local Randomization RD analysis, Local Polynomial Order
- Quantile treatment effects in the RD
- Window selection based on covariates
- Spatial RD
- Regression Discontinuity in Time
- Difference-in-differences, Parallel Trends, Triple difference
- DID with Covariates, Two-way Fixed Effects (TWFE)
- Nonbinary response (Binary, Fractional, Nonnegative), ratio in ratios, ratio in odds-ratios
- Differential timing, Bacon decomposition
- Group-time average treatment effects
- Imputation Estimators, Mundlak Estimator, Fuzzy Difference-in-differences
- Synthetic control
- Synthetic difference-in-differences
- Causal Latent Variable Models
Practical classes are taught in R and Stata. Participants are asked to install the software before the course.
Schedule:
13.01.2025 - 16.01.2025
15:00-21:30 GMT+1(Warsaw, Madrid, Berlin, Rome)
9:00-15:30 EST (Boston, New York, Washington)
24 hours contact hours