Comparar métodos

	Algoritmos de Descoberta Causal (PC, FCI, LiNGAM)	Método de Variáveis Instrumentais (VI) para Inferência Causal
Área	Inferência causal	Economia da saúde
Família	Regression model	Process / pipeline
Ano de origem	2000	1990s (modern applications)
Autor original	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Causal structure learning	Method
Fonte 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: Princeton University Press.
Outros nomes	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	IV, two-stage least squares, TSLS, causal estimation
Relacionados	5	3
Resumo	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.	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.