방법 비교

	Difference-in-Means Estimator	Survey Experiment
분야	Political Science	Political Science
계열	Process / pipeline	Process / pipeline
기원 연도	1923	2011
창시자	Jerzy Neyman (design-based potential-outcomes framework)	Experimental political science; synthesized by Diana Mutz
유형	Design-based estimator of the average treatment effect	Randomized experiment embedded in a survey
원전	Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. New York: W. W. Norton. ISBN: 9780393979954	Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528
별칭	Neyman estimator, Design-based ATE estimator, Difference of sample means, Mean-difference treatment effect estimator	Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
관련	4	4
요약	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.	A survey experiment embeds a randomized experiment inside a survey: respondents are randomly assigned to different versions of a question, frame, or stimulus, and their answers are compared to estimate a causal effect. By combining the internal validity of randomization with the representative samples and rich measurement of survey research, survey experiments — especially population-based ones — let political scientists draw causal inferences about how information, framing, or message attributes shape public attitudes and behavior.