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Взвешивание на основе показателя склонности в образовательных исследованиях×Регрессионный разрывный дизайн (RDD)×
ОбластьПричинно-следственный выводПричинно-следственный вывод
СемействоRegression modelRegression model
Год появления1983 (theory); widely adopted in education research from 2000s2008
Автор методаRosenbaum & Rubin (foundational theory, 1983); Thoemmes & Kim (education-focused review, 2011)Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
ТипQuasi-experimental causal inferenceQuasi-experimental causal design
Основополагающий источникRosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. DOI ↗Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗
Другие названияPSW in education, inverse probability weighting in education, IPW education, propensity weighting educationRDD, regression discontinuity design, sharp RDD, fuzzy RDD
Связанные55
СводкаPropensity score weighting (PSW) is a quasi-experimental technique that reweights observational samples so that treated and comparison students look similar on measured background characteristics, allowing credible causal estimates of educational interventions — such as program participation, instructional method, or school type — without random assignment.Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.
ScholarGateНабор данных
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  2. 2 Источники
  3. PUBLISHED
  1. v1
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ScholarGateСравнение методов: Propensity Score Weighting in Education Research · Regression Discontinuity. Получено 2026-06-20 из https://scholargate.app/ru/compare