So sánh phương pháp

	Thí nghiệm tự nhiên có khối	Thí nghiệm tự nhiên
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	Blocking: 1935; natural experiments as formal causal framework: 1990s–2000s	1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854)
Người khởi xướng	Combines Fisher's blocking principle (1935) with natural experiment methodology formalized by Angrist and Pischke (2009)	Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991)
Loại	Quasi-experimental causal design	Quasi-experimental research design
Công trình gốc	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355	Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13(2), 151–161.
Tên gọi khác	stratified natural experiment, block-stratified quasi-experiment, natural experiment with blocking	natural quasi-experiment, naturally occurring experiment, exogenous shock design, as-if randomization
Liên quan	3	3
Tóm tắt	A blocked natural experiment is a quasi-experimental design that exploits naturally occurring, researcher-uncontrolled variation in treatment assignment while pre-stratifying (blocking) units on key observed covariates. Blocking absorbs between-stratum variance, improves statistical precision, and strengthens the plausibility of the as-if-random assumption within each block. The design draws on Fisher's blocking principle and the natural experiment tradition in economics and epidemiology.	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.