Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Natural Experiment in Politics× | Difference-in-Means Estimator× | |
|---|---|---|
| Domaine | Political Science | Political Science |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2012 | 1923 |
| Auteur d'origine≠ | Dunning (design-based framework); Lee (close-election RD lineage) | Jerzy Neyman (design-based potential-outcomes framework) |
| Type≠ | Observational study exploiting as-if random assignment | Design-based estimator of the average treatment effect |
| Source fondatrice≠ | 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. (2012). Field Experiments: Design, Analysis, and Interpretation. New York: W. W. Norton. ISBN: 9780393979954 |
| Alias | Political natural experiment, As-if random design, Design-based natural experiment, Quasi-experiment with as-if randomization | Neyman estimator, Design-based ATE estimator, Difference of sample means, Mean-difference treatment effect estimator |
| Apparentées | 4 | 4 |
| Résumé≠ | 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. | The difference-in-means estimator is the design-based workhorse for analyzing randomized experiments: it estimates the average treatment effect simply as the difference between the average outcome among treated units and the average outcome among control units. Rooted in Jerzy Neyman's potential-outcomes framework and central to modern treatments by Imbens and Rubin and by Gerber and Green, it is unbiased under randomization, comes with a conservative Neyman variance estimator, and supports exact randomization inference, requiring no model of how outcomes are generated. |
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