Порівняння методів

	Динамічне зіставлення за показником схильності	Подвійне робастне оцінювання (AIPW)
Галузь	Причинно-наслідковий висновок	Причинно-наслідковий висновок
Родина	Regression model	Regression model
Рік появи	1986-2010	2005
Автор методу	Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching	Robins & Rotnitzky; Bang & Robins
Тип	Sequential causal matching	Semiparametric causal estimator
Основоположне джерело	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.	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.
Інші назви	dynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSM	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Пов'язані	6	5
Підсумок	Dynamic 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.