Sammenlign metoder

	Dynamisk propensity score-matching	Propensity Score Matching
Fagområde	Kausal inferens	Forskningsstatistik
Familie	Regression model	Process / pipeline
Oprindelsesår	1986-2010	1983
Ophavsperson	Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching	Paul Rosenbaum and Donald Rubin
Type	Sequential causal matching	Method
Oprindelig kilde	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.
Aliasser	dynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSM	PSM, propensity score weighting, covariate balance
Relaterede	6	3
Resumé	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.	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.