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Dynamic Propensity Score Matching×Двухробастное оценивание (AIPW)×
ОбластьПричинно-следственный выводПричинно-следственный вывод
СемействоRegression modelRegression model
Год появления1986-20102005
Автор методаRobins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matchingRobins & Rotnitzky; Bang & Robins
ТипSequential causal matchingSemiparametric causal estimator
Основополагающий источникLechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137. DOI ↗Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗
Другие названияdynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSMAIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Связанные65
СводкаDynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each decision point using the full history of covariates and prior treatments.Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.
ScholarGateНабор данных
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  2. 2 Источники
  3. PUBLISHED
  1. v1
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ScholarGateСравнение методов: Dynamic Propensity Score Matching · Doubly Robust Estimation. Получено 2026-06-18 из https://scholargate.app/ru/compare