So sánh phương pháp

	Thí nghiệm tự nhiên mù đơn	Thí nghiệm tự nhiên
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	Late 20th century (formalized practice)	1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854)
Người khởi xướng	Conceptual synthesis of natural experiment tradition (Haavelmo, 1944; Campbell & Stanley, 1963) with single-blind methodology	Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991)
Loại	Quasi-experimental design with partial blinding	Quasi-experimental research design
Công trình gốc	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.
Tên gọi khác	single-masked natural experiment, blinded quasi-experiment, single-blind exogenous assignment study	natural quasi-experiment, naturally occurring experiment, exogenous shock design, as-if randomization
Liên quan	2	3
Tóm tắt	A single-blind natural experiment leverages an exogenous, researcher-uncontrolled event — such as a policy change, lottery, or natural disaster — to create treatment and comparison groups, while applying single-blind procedures so that either the participants or the outcome assessors (but not both) are unaware of group assignment. This design combines the causal leverage of natural variation with reduced measurement bias from blinding.	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.