방법 비교

	Policy Evaluation Instrumental Variables	국소 평균 처리 효과 (LATE / CACE)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	1996 (modern policy-evaluation framing); IV roots 1920s	1994
창시자	Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953)	Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)
유형	Quasi-experimental causal inference / IV regression	Instrumental-variable causal estimand
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.	Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.
별칭	IV policy evaluation, 2SLS policy analysis, natural-experiment IV, policy IV estimation	LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)
관련	5	5
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.	The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis.