Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Difference-in-Means Estimator× | Audit Experiment× | |
|---|---|---|
| Domeniu | Political Science | Political Science |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1923 | 2011 |
| Autorul original≠ | Jerzy Neyman (design-based potential-outcomes framework) | Butler & Broockman (political responsiveness audits); Bertrand & Mullainathan (correspondence-audit lineage) |
| Tip≠ | Design-based estimator of the average treatment effect | Randomized field experiment using matched fictitious requests |
| Sursa seminală≠ | Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. New York: W. W. Norton. ISBN: 9780393979954 | Butler, D. M., & Broockman, D. E. (2011). Do Politicians Racially Discriminate Against Constituents? A Field Experiment on State Legislators. American Journal of Political Science, 55(3), 463–477. DOI ↗ |
| Denumiri alternative | Neyman estimator, Design-based ATE estimator, Difference of sample means, Mean-difference treatment effect estimator | Correspondence study, Field audit study, Discrimination audit, Responsiveness audit |
| Înrudite | 4 | 4 |
| Rezumat≠ | 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. | An audit experiment, also called a correspondence or field audit study, sends matched but fictitious requests to real-world targets — such as legislators, landlords, or employers — while randomizing a single treatment cue, then compares the rate and quality of responses. In political science the canonical design follows Butler and Broockman's 2011 study of U.S. state legislators, which varied the putative race signaled by a constituent's name to measure discrimination in responsiveness. |
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