Comparar métodos

	Evaluación de políticas con variables instrumentales	Diferencia en Diferencias (Diff-in-Diff)
Campo	Inferencia causal	Econometría
Familia	Regression model	Regression model
Año de origen	1996 (modern policy-evaluation framing); IV roots 1920s	1994
Autor original	Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tipo	Quasi-experimental causal inference / IV regression	Causal inference / panel regression
Fuente seminal	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 University Press. ISBN: 978-0691120355
Alias	IV policy evaluation, 2SLS policy analysis, natural-experiment IV, policy IV estimation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados	5	5
Resumen	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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.