방법 비교

	Policy Evaluation Instrumental Variables	인과 추론을 위한 도구 변수(IV) 방법
분야	인과추론	보건경제학
계열	Regression model	Process / pipeline
기원 연도	1996 (modern policy-evaluation framing); IV roots 1920s	1990s (modern applications)
창시자	Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953)	Angrist & Pischke (applied econometrics); rooted in econometric theory
유형	Quasi-experimental causal inference / IV regression	Method
원전	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.
별칭	IV policy evaluation, 2SLS policy analysis, natural-experiment IV, policy IV estimation	IV, two-stage least squares, TSLS, causal estimation
관련	5	3
요약	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.