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| Pilotnaturligt experiment× | Naturligt experiment× | |
|---|---|---|
| Ämnesområde | Försöksplanering | Försöksplanering |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 2000s–2010s (as formalized practice) | 1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854) |
| Upphovsperson≠ | Combination of natural experiment tradition (Dunning, Angrist, Pischke) and pilot study methodology | Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991) |
| Typ≠ | Quasi-experimental feasibility design | Quasi-experimental research design |
| Ursprungskälla≠ | Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press. ISBN: 9781107017412 | Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13(2), 151–161. DOI ↗ |
| Alias | feasibility natural experiment, preliminary natural experiment, pilot quasi-experiment, exploratory natural experiment | natural quasi-experiment, naturally occurring experiment, exogenous shock design, as-if randomization |
| Närliggande≠ | 4 | 3 |
| Sammanfattning≠ | A pilot natural experiment is a small-scale preliminary study that exploits an existing exogenous event or policy variation to test whether a full natural experiment is viable. It preserves the core logic of natural experiments — using real-world discontinuities to approximate causal inference — while explicitly scoping the work to assess data availability, group comparability, effect detectability, and procedural feasibility before committing resources to a larger study. | A natural experiment exploits a real-world event, policy, or circumstance that assigns individuals to treatment and control conditions in a way that is plausibly random — or at least exogenous to the outcome of interest. Because the researcher does not control assignment, it occupies a middle ground between a true randomized controlled trial and purely observational research, offering stronger causal leverage than conventional observational designs when the as-if randomization assumption holds. |
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