Quantitative/ Study design
Quasi-experiment
Learn from a change you did not assign.
Estimate intervention effects without randomised assignment by exploiting a credible comparison structure. Designs include difference-in-differences, regression discontinuity, and interrupted time series.
WHEN IT FITS
Randomisation is unavailable, but a policy change, cutoff, or timing pattern can support a defensible counterfactual.
Strengths
- Can evaluate real-world policies at scale.
- Uses existing variation or administrative data.
- Makes causal assumptions explicit and examinable.
Limitations
- Identification rests on design-specific assumptions.
- Concurrent changes may undermine the comparison.
- Some designs identify effects only near a cutoff or in specific groups.
Know the boundary
A before-and-after difference by itself is not a credible causal estimate. The counterfactual argument is the heart of the design.
USED ACROSS
Banking & financeBusiness & MBAPsychologyComputer scienceEducation