Linganisha mbinu

	Tathmini ya Sera kwa Kutumia Vigezo vya Ala	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	1996 (modern policy-evaluation framing); IV roots 1920s	1990s (modern applications)
Mwanzilishi	Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Aina	Quasi-experimental causal inference / IV regression	Method
Chanzo asilia	Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association, 91(434), 444-455.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Majina mbadala	IV policy evaluation, 2SLS policy analysis, natural-experiment IV, policy IV estimation	IV, two-stage least squares, TSLS, causal estimation
Zinazohusiana	5	3
Muhtasari	Instrumental Variables (IV) estimation for policy evaluation is a quasi-experimental technique that uses an exogenous instrument — a variable that shifts exposure to a policy but is otherwise unrelated to the outcome — to recover the causal effect of a program or intervention from non-experimental data. Popularised in policy research by Angrist, Imbens, and Rubin (1996), it identifies the Local Average Treatment Effect (LATE) among units whose treatment status is changed by the instrument.	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.