Comparer des méthodes

	Expérience naturelle factorielle	Expérience naturelle
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1920s (factorial origins, Fisher); natural experiment formalization 1990s–2000s; factorial natural experiment usage widespread 2000s–present	1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854)
Auteur d'origine	Extension of natural experiment tradition (Dunning, Angrist & Pischke) combined with factorial design logic (Fisher)	Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991)
Type	Quasi-experimental research design	Quasi-experimental research design
Source fondatrice	Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press. ISBN: 978-1107698000	Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13(2), 151–161.
Alias	factorial quasi-experiment, multi-factor natural experiment, factorial exogenous variation design	natural quasi-experiment, naturally occurring experiment, exogenous shock design, as-if randomization
Apparentées	4	3
Résumé	A factorial natural experiment exploits naturally occurring exogenous variation across two or more factors simultaneously, allowing researchers to estimate main effects and interactions without random assignment. Natural events, policy changes, or institutional rules create treatment conditions that approximate a factorial structure, enabling causal inference in observational settings where controlled experimentation is infeasible or unethical.	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.