Comparar métodos

	Natural Experiment in Politics	Field Experiment in Politics
Área	Political Science	Political Science
Família	Process / pipeline	Process / pipeline
Ano de origem	2012	2000
Autor original	Dunning (design-based framework); Lee (close-election RD lineage)	Gerber & Green (modern political field experiments)
Tipo	Observational study exploiting as-if random assignment	Randomized experiment conducted in a real political setting
Fonte seminal	Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge: Cambridge University Press. ISBN: 9781107698000	Gerber, A. S., & Green, D. P. (2000). The Effects of Canvassing, Telephone Calls, and Direct Mail on Voter Turnout: A Field Experiment. American Political Science Review, 94(3), 653–663.
Outros nomes	Political natural experiment, As-if random design, Design-based natural experiment, Quasi-experiment with as-if randomization	Political field experiment, Get-out-the-vote experiment, GOTV experiment, Voter mobilization experiment
Relacionados	4	4
Resumo	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 field experiment in political science randomizes a real intervention — such as a get-out-the-vote canvass, mailing, or phone call — among genuine political actors in their natural environment and compares behavioral outcomes like validated turnout. Revived for the discipline by Gerber and Green's 2000 voter-mobilization study and codified in their 2012 textbook, the approach combines the causal leverage of randomization with the realism of consequential, real-world settings, while carefully distinguishing the effect of being assigned a treatment from the effect of actually receiving it.