方法对比

	Field Experiment in Politics	Natural Experiment in Politics
领域	Political Science	Political Science
方法族	Process / pipeline	Process / pipeline
起源年份	2000	2012
提出者	Gerber & Green (modern political field experiments)	Dunning (design-based framework); Lee (close-election RD lineage)
类型	Randomized experiment conducted in a real political setting	Observational study exploiting as-if random assignment
开创性文献	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.	Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge: Cambridge University Press. ISBN: 9781107698000
别名	Political field experiment, Get-out-the-vote experiment, GOTV experiment, Voter mobilization experiment	Political natural experiment, As-if random design, Design-based natural experiment, Quasi-experiment with as-if randomization
相关	4	4
摘要	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.	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.