Сравнение методов

	Inverse Probability Weighting in Education Research	Регрессионный разрывный дизайн (RDD)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	1983–2003	2008
Автор метода	Rosenbaum & Rubin (propensity score, 1983); Hirano, Imbens & Ridder (efficient IPW, 2003)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Тип	Causal weighting estimator	Quasi-experimental causal design
Основополагающий источник	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Другие названия	IPW in education, propensity-weighted analysis, IPTW education, inverse probability treatment weighting	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Связанные	6	5
Сводка	Inverse Probability Weighting (IPW) is a causal inference technique that reweights observational education data to mimic a randomised experiment. Each student or school is assigned a weight equal to the inverse of the probability they received the treatment — thereby creating a pseudo-population in which programme participation is independent of measured background characteristics. The method is widely used in education research to evaluate school programmes, interventions, and policies from administrative or survey data.	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.