Daily Content

Day 1
Potential outcomes and parameters of interest. Randomization. Unconfoundedness and overlap. Identification. Regression adjustment, inverse probability weighting, normalized weights. Covariate balancing propensity score estimation. Matching and doubly robust estimators. Improved efficiency under randomized controlled trials. Multiple treatments.
Day 2
Potential treatment status. Constant effect model. Compliers and defiers. One-sided non-compliance. Identification of LATE and LATT. Including covariates for LATE and LATT estimation. Control function methods. Regression discontinuity (if time permits).
Day 3
Two-period difference-in-differences; no anticipation and parallel trends; controlling for covariates via regression adjustment and propensity score methods. General common timing case and heterogeneous effects.
Day 4
Staggered interventions. Parameters of interest. Parallel trends. Shortcomings of the constant effect model. Regression-based methods. Two-period difference-in-differences; no anticipation and parallel trends; controlling for covariates via regression adjustment and propensity score methods. Repeated cross sections.
Day 5
Parallel trends in nonlinear models. Nonlinear DiD with two periods. Staggered interventions. Adding covariates. Binary, fractional, and nonnegative responses (count, corner). Imputation estimates. Pooled quasi-maximum likelihood estimation. Estimation with a canonical link function. Repeated cross sections.