Programme and policy evaluation
Programmes are usually judged on activity: people served, dollars spent. Funders and boards want something harder, which is what changed because of the programme, compared with what would have happened anyway.
Questions this answers
- Did the programme move the outcome it was designed to move, and by how much?
- Did the effect differ across regions, income groups or types of participant?
- Is the effect large enough to justify continuing or expanding it?
- What can the data you already hold settle, and what would a stronger evaluation need?
Methods
Difference-in-differences and event-study designs, matching and weighting, regression discontinuity, interrupted time series, synthetic control, and randomised designs where they are feasible. The estimand, the precise quantity being measured, is fixed before any analysis starts.
Other services
Did your AI spending pay off?
An independent measure of what your AI tools changed in output, cost and quality, against what would have happened without them.
Pay-gap and pay-equity analysis
A defensible pay-gap analysis for reporting rules, and a clear view of which gaps are explained and which are not.
Health economics
Cost-effectiveness, budget impact and model audits that hold up in front of reviewers.