方法对比

	空间因果敏感性分析	因果推断的工具变量（IV）方法
领域	因果推断	卫生经济学
方法族	Regression model	Process / pipeline
起源年份	1988–2021 (developed progressively)	1990s (modern applications)
提出者	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Angrist & Pischke (applied econometrics); rooted in econometric theory
类型	Sensitivity / robustness analysis	Method
开创性文献	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. ISBN: 978-9024737322	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
别名	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	IV, two-stage least squares, TSLS, causal estimation
相关	6	3
摘要	Spatial sensitivity analysis for causality systematically tests whether a causal estimate derived from georeferenced data holds up as spatial structure, spillovers, and the choice of spatial weights matrix are varied. Because nearby units often share unmeasured confounders — soil quality, local infrastructure, neighbourhood norms — a naive regression may yield biased causal estimates. This method reveals how fragile or robust a claimed causal effect is to alternative spatial specifications.	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.