Salīdzināt metodes

	Politikas novērtēšana ar instrumentālajiem mainīgajiem	Instrumentālo mainīgo (IV) metode cēloņsakarību noteikšanai
Nozare	Cēloņsakarību secināšana	Veselības ekonomika
Saime	Regression model	Process / pipeline
Izcelsmes gads	1996 (modern policy-evaluation framing); IV roots 1920s	1990s (modern applications)
Autors	Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tips	Quasi-experimental causal inference / IV regression	Method
Pirmavots	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.
Citi nosaukumi	IV policy evaluation, 2SLS policy analysis, natural-experiment IV, policy IV estimation	IV, two-stage least squares, TSLS, causal estimation
Saistītās	5	3
Kopsavilkums	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.