方法对比

	Natural Experiment in Politics	自然实验
领域	Political Science	实验设计
方法族	Process / pipeline	Process / pipeline
起源年份	2012	1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854)
提出者	Dunning (design-based framework); Lee (close-election RD lineage)	Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991)
类型	Observational study exploiting as-if random assignment	Quasi-experimental research design
开创性文献	Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge: Cambridge University Press. ISBN: 9781107698000	Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13(2), 151–161.
别名	Political natural experiment, As-if random design, Design-based natural experiment, Quasi-experiment with as-if randomization	natural quasi-experiment, naturally occurring experiment, exogenous shock design, as-if randomization
相关	4	3
摘要	A natural experiment in political science exploits a naturally occurring source of as-if random assignment — close elections, lotteries, arbitrary boundaries, or policy thresholds — to identify causal effects without the researcher manipulating anything. Codified for the social sciences by Thad Dunning's 2012 design-based treatment and exemplified by David Lee's close-election regression-discontinuity analysis of U.S. House races, the approach treats nature, institutions, or chance as if they had run an experiment, recovering credible causal estimates from observational data when randomization is impossible.	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.