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.
Leer el método completo
Inicia sesión con una cuenta gratuita para leer esta sección.
Mapa de métodos
El vecindario de métodos relacionados: selecciona un nodo para explorarlo.
Fuentes
- 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
Cómo citar esta página
ScholarGate. (2026, June 22). Difference-in-Means Estimator for Randomized Experiments. ScholarGate. https://scholargate.app/es/political-science/difference-in-means-experiment
¿Qué método?
Coloca este método junto a sus parientes más cercanos y léelos lado a lado: la biblioteca pone los libros sobre la mesa; la elección es tuya.
- Audit ExperimentPolitical Science↔ comparar
- Field Experiment in PoliticsPolitical Science↔ comparar
- Natural Experiment in PoliticsPolitical Science↔ comparar
- Survey ExperimentPolitical Science↔ comparar
Citado por
¿Has visto un problema en esta página? Infórmanos o sugiere una corrección →