Comparar métodos

	Mínimos Cuadrados Ordinarios (MCO)	Método de Variables Instrumentales (VI) para Inferencia Causal
Campo	Estadística	Economía de la salud
Familia	Regression model	Process / pipeline
Año de origen	1805	1990s (modern applications)
Autor original	Adrien-Marie Legendre (1805); Carl Friedrich Gauss (1809)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Linear parameter estimation	Method
Fuente seminal	Legendre, A.-M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la Méthode des moindres quarrés, pp. 72–80.]	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alias	OLS, OLS regression, linear least squares, classical linear regression	IV, two-stage least squares, TSLS, causal estimation
Relacionados	8	3
Resumen	Ordinary Least Squares (OLS) is the canonical method for estimating the parameters of a linear regression model by minimizing the sum of squared differences between observed and predicted values. First published by Adrien-Marie Legendre in 1805 and independently developed by Carl Friedrich Gauss (who claimed priority from 1795), OLS is provably optimal under the Gauss-Markov theorem: given its assumptions, it yields the Best Linear Unbiased Estimator (BLUE) of the regression coefficients.	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.