Compară metode

	Estimator de potrivire dinamică	Potrivirea scorului de propensitate
Domeniu	Inferență cauzală	Statistică pentru cercetare
Familie	Regression model	Process / pipeline
Anul apariției	2010	1983
Autorul original	Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)	Paul Rosenbaum and Donald Rubin
Tip	Nonparametric causal inference / matching	Method
Sursa seminală	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Denumiri alternative	dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME	PSM, propensity score weighting, covariate balance
Înrudite	6	3
Rezumat	The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.