Data & Methods for Policy
Knowing whether a policy worked — and measuring it honestly.
The applied-methods specialization for policy: impact evaluation and randomised trials, the quasi-experimental toolkit, and the survey design and measurement that every policy number depends on. Pairs with the Econometrics, Stata, and R courses in the Data Analysis path.
By the end
- ✓Design an impact evaluation and defend its identification strategy
- ✓Choose between RCT, diff-in-diff, RD, and IV for a given question
- ✓Read a published evaluation and find its threats to validity
- ✓Build a welfare or inequality measure from survey microdata
- ✓Judge a composite indicator by its weighting and aggregation choices
Prereqs
- •Intro statistics
- •The Econometrics course helps but isn't required
Courses
Impact Evaluation & Randomised Trials
AdvancedHow to know whether a policy actually worked. The counterfactual problem, the randomised controlled trial and the credibility revolution, quasi-experimental methods when you can't randomise, and the leap from a clean estimate to a policy decision.
Survey Data, Measurement & Indicators
IntermediateThe unglamorous foundation every policy number rests on. Survey design and sampling, measuring welfare and inequality, composite indicators, administrative and satellite data, and the data quality, ethics, and reproducibility that decide whether the number means anything.