Comparar métodos

	Algoritmos de descubrimiento causal (PC, FCI, LiNGAM)	Diferencia en Diferencias (Diff-in-Diff)	Método de Variables Instrumentales (VI) para Inferencia Causal
Campo	Inferencia causal	Econometría	Economía de la salud
Familia	Regression model	Regression model	Process / pipeline
Año de origen	2000	1994	1990s (modern applications)
Autor original	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Causal structure learning	Causal inference / panel regression	Method
Fuente seminal	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alias	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)	IV, two-stage least squares, TSLS, causal estimation
Relacionados	5	5	3
Resumen	Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges.	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.	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.