Módszerek összehasonlítása

	Instrumentális Változók (IV) Módszer Kauzális Infláció Becslésére	Markov-lánc modell az egészségügy közgazdaságtanában
Tudományterület	Egészség-gazdaságtan	Egészség-gazdaságtan
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	1990s (modern applications)	1983
Megalkotó	Angrist & Pischke (applied econometrics); rooted in econometric theory	Beck & Pauker (medical decision analysis, Massachusetts General Hospital)
Típus	Method	Method
Alapmű	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Beck, J. R., & Pauker, S. G. (1983). The Markov Process in Medical Prognosis. Medical Decision Making, 3(4), 419-458.
Alternatív nevek	IV, two-stage least squares, TSLS, causal estimation	Markov model, state transition model, cohort simulation
Kapcsolódó	3	5
Összefoglaló	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.	A Markov model is a decision-analytic tool that simulates disease progression through defined health states over time, calculating cumulative costs and quality-adjusted life years (QALYs) to enable cost-effectiveness analysis. Developed by Beck and Pauker in 1983, Markov models are now the standard framework for projecting long-term outcomes of health interventions, especially chronic diseases where patients transition between clinical states (treatment response, disease progression, remission, death). Used by health technology assessment bodies and pharmaceutical companies to predict intervention value beyond trial duration.