Linganisha mbinu

	Mtafsiri wa Makadirio ya Mechi ya Tathmini ya Sera	Njia ya Vigezo vya Ala (IV) kwa Utafutaji wa Kifungo
Nyanja	Uhitimisho wa Kisababishi	Uchumi wa Afya
Familia	Regression model	Process / pipeline
Mwaka wa asili	1998-2006	1990s (modern applications)
Mwanzilishi	Heckman, Ichimura & Todd; Abadie & Imbens	Angrist & Pischke (applied econometrics); rooted in econometric theory
Aina	Non-parametric causal estimator	Method
Chanzo asilia	Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Majina mbadala	matching estimator, program evaluation matching, treatment effect matching, Abadie-Imbens estimator	IV, two-stage least squares, TSLS, causal estimation
Zinazohusiana	6	3
Muhtasari	The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation.	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.