3. Policy Evaluation Designs
Construct untreated outcomes from trends, thresholds, comparable units and weighted donor paths
3. Policy Evaluation Designs
Each design supplies a different answer to the missing-counterfactual problem. Similar estimates across designs are informative only after their estimands and assumptions are compared.
Learning outcomes
After this module, you should be able to:
- derive a 2×2 DID and state parallel trends in potential-outcome terms;
- replace inappropriate staggered TWFE summaries with group-time effects;
- design sharp and fuzzy RDD analyses around a local cutoff estimand;
- assess overlap, balance and extreme weights in matching/IPW;
- construct and falsify a synthetic-control counterfactual;
- explain which diagnostic targets which identifying assumption.
Chapter route
- Difference-in-Differences — use an untreated trend as the missing change.
- Staggered DID and Event Studies — separate cohorts, horizons and sensitivity.
- Regression Discontinuity — compare units at an assignment cutoff.
- Matching and Weighting — build observable comparability and expose overlap.
- Synthetic Control — construct a weighted untreated path for aggregate interventions.
Prepare, work, follow up
- Prepare (75 min): bring a policy timeline, threshold rule or treatment-assignment table from a published study.
- Workshop (120 min): calculate DID and RDD effects; reject a poor donor pool; diagnose a propensity-score overlap failure.
- Follow up (75 min): write a design comparison in which each robustness check is linked to one named threat.
Postgraduate extension: define group-time ATT, robust bias-corrected RD inference, doubly robust ATT and the factor structure underlying synthetic-control fit.