방법 비교

	Natural Experiment in Politics	자연 실험
분야	Political Science	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	2012	1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854)
창시자	Dunning (design-based framework); Lee (close-election RD lineage)	Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991)
유형	Observational study exploiting as-if random assignment	Quasi-experimental research design
원전	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.
별칭	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
관련	4	3
요약	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.