Difference-in-Means Estimator
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.
Lire la méthode complète
Connectez-vous avec un compte gratuit pour lire cette section.
Carte des méthodes
Le voisinage des méthodes apparentées — sélectionnez un nœud pour explorer.
Sources
- Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. New York: W. W. Norton. ISBN: 9780393979954
- Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge: Cambridge University Press. ISBN: 9780521885881
Comment citer cette page
ScholarGate. (2026, June 22). Difference-in-Means Estimator for Randomized Experiments. ScholarGate. https://scholargate.app/fr/political-science/difference-in-means-experiment
Quelle méthode ?
Placez cette méthode aux côtés de ses plus proches parentes et lisez-les côte à côte — la bibliothèque pose les ouvrages sur la table ; le choix vous revient.
- Audit ExperimentPolitical Science↔ comparer
- Field Experiment in PoliticsPolitical Science↔ comparer
- Natural Experiment in PoliticsPolitical Science↔ comparer
- Survey ExperimentPolitical Science↔ comparer
Référencée par
Méthodes similaires
Une erreur sur cette page ? Signalez-la ou proposez une correction →