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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.

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Difference-in-Means Estimator for Randomized Experiments

Taksonoomiline meetodikirje · process-pipeline / political-science

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

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