विधियों की तुलना करें

	Field Experiment in Politics	Difference-in-Means Estimator
क्षेत्र	Political Science	Political Science
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2000	1923
प्रवर्तक	Gerber & Green (modern political field experiments)	Jerzy Neyman (design-based potential-outcomes framework)
प्रकार	Randomized experiment conducted in a real political setting	Design-based estimator of the average treatment effect
मौलिक स्रोत	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.	Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. New York: W. W. Norton. ISBN: 9780393979954
उपनाम	Political field experiment, Get-out-the-vote experiment, GOTV experiment, Voter mobilization experiment	Neyman estimator, Design-based ATE estimator, Difference of sample means, Mean-difference treatment effect estimator
संबंधित	4	4
सारांश	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.	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.