Quasi-Experiment
Definition
A quasi-experiment examines effects without random assignment by researchers.
An investigative design to estimate causal effects without random assignment by researchers. It uses a justified comparison design and design-dependent identification assumptions.
Simply explained
Instead of randomly assigning participants, one compares groups selected with justification.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A staggered rollout is evaluated with a pre-justified comparison group and difference-in-differences.
Limits and pitfalls
- Ignoring the absence of randomization, interpreting every before-after design causally, or failing to check design-dependent assumptions such as parallel trends, continuity, or exclusion restrictions.
Origin & history
Quasi-experiments are established in econometrics, social science, and evaluation.
Use in mid-sized companies
- Possible application: A staggered rollout is evaluated using a pre-justified comparison group and Difference-in-Differences.
Frequently asked questions
What does quasi-experiment mean?
A study design for estimating causal effects without random assignment by the researchers. It uses a justified comparison design and design-dependent identification assumptions. What mistakes should be avoided? Ignoring the absence of randomization, interpreting every before-after design causally, or failing to check design-dependent assumptions such as parallel trends, continuity, or exclusion restrictions.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
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