Comparar métodos

	Estimación Robusta de Variables Instrumentales	Diferencia en Diferencias (Diff-in-Diff)
Campo	Inferencia causal	Econometría
Familia	Regression model	Regression model
Año de origen	1949–2019	1994
Autor original	Anderson & Rubin (1949); Stock, Wright & Yogo (2002); Andrews, Stock & Sun (2019)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tipo	Causal inference / robust estimation	Causal inference / panel regression
Fuente seminal	Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518-529.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	Robust IV, Weak-instrument-robust IV, Robust 2SLS, Weak-instrument-robust inference	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados	4	5
Resumen	Robust Instrumental Variables estimation extends standard IV and two-stage least squares (2SLS) by guarding against weak-instrument bias and non-standard inference. Methods such as the Anderson-Rubin test, Limited Information Maximum Likelihood (LIML), and the Conditional Likelihood Ratio test provide valid confidence sets and hypothesis tests even when instruments are weak or only partially identified, making IV inference reliable in settings where standard 2SLS breaks down.	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.