Порівняння методів

	Односліпий польовий експеримент	Природний експеримент
Галузь	Планування експерименту	Планування експерименту
Родина	Process / pipeline	Process / pipeline
Рік появи	Mid-20th century (blinding conventions formalised 1940s–1960s)	1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854)
Автор методу	Established practice in experimental social science and clinical research; codified by Campbell & Stanley (1963) and Shadish, Cook & Campbell (2002)	Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991)
Тип	Controlled field experiment with partial blinding	Quasi-experimental research design
Основоположне джерело	Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560	Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13(2), 151–161.
Інші назви	single-masked field experiment, field experiment with single blinding, single-blind natural-setting trial	natural quasi-experiment, naturally occurring experiment, exogenous shock design, as-if randomization
Пов'язані	6	3
Підсумок	A single-blind field experiment combines real-world experimental conditions with partial blinding: either participants or outcome assessors — but not both — are kept unaware of treatment assignment. This design reduces demand characteristics or observer bias while preserving ecological validity, making it a practical middle ground when full double-blinding is logistically infeasible in naturalistic settings.	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.