Módszerek összehasonlítása

	Heterogén Kezelési Hatás Marginális Strukturális Modell (HTE-MSM)	Az inverz valószínűségi kezelési súlyozás (IPW / IPTW)
Tudományterület	Oksági következtetés	Oksági következtetés
Módszercsalád	Regression model	Regression model
Keletkezés éve	2000–2010s	2000
Megalkotó	Robins, Hernan & Brumback (foundational MSM framework, 2000); heterogeneous-effect extensions developed throughout 2000s–2010s	Robins, Hernán & Brumback
Típus	Causal inference / weighted regression with effect modification	Causal inference weighting estimator
Alapmű	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alternatív nevek	HTE-MSM, heterogeneous MSM, subgroup MSM, effect-modified marginal structural model	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Kapcsolódó	5	5
Összefoglaló	The Heterogeneous Treatment Effect Marginal Structural Model extends the classic MSM framework of Robins, Hernan, and Brumback to estimate how treatment effects vary across subgroups or individual-level moderators. By weighting observations with inverse probability of treatment weights (IPTW) and interacting the treatment with effect modifiers in the weighted outcome model, the approach produces subgroup-specific or continuous causal effect estimates from observational data.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.