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| Ανάλυση Χωρικής Ευαισθησίας για την Αιτιότητα× | Μέθοδος Εργαλειακών Μεταβλητών (IV) για Αιτιώδη Συμπερασματολογία× | |
|---|---|---|
| Πεδίο≠ | Αιτιακή Συμπερασματολογία | Οικονομικά της Υγείας |
| Οικογένεια≠ | Regression model | Process / pipeline |
| Έτος προέλευσης≠ | 1988–2021 (developed progressively) | 1990s (modern applications) |
| Δημιουργός≠ | Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Τύπος≠ | Sensitivity / robustness analysis | Method |
| Θεμελιώδης πηγή≠ | 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. link ↗ |
| Εναλλακτικές ονομασίες | spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity | IV, two-stage least squares, TSLS, causal estimation |
| Συναφείς≠ | 6 | 3 |
| Σύνοψη≠ | 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. |
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