Rekodi ya ushahidi wa mbinu

Spatial Sensitivity Analysis for Causality

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.

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Spatial Sensitivity Analysis for Causal Inference

Rekodi ya mbinu ya kiajenda · regression-model / causal-inference

Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. · ISBN 978-9024737322
Reich, B. J., Yang, S., Guan, Y., Giffin, A. B., Miller, M. J., & Rappold, A. G. (2021). A review of spatial causal inference methods for environmental and epidemiological applications. International Statistical Review, 89(3), 605-634. · DOI 10.1111/insr.12452

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Same method familyDifference-in-Differencesmachine-suggested · Relational suggestion, not evidence.Same method familyGeographically Weighted Regressionmachine-suggested · Relational suggestion, not evidence.See alsoInstrumental Variables in Health Researchmachine-suggested · Relational suggestion, not evidence.See alsoPropensity Score Matchingmachine-suggested · Relational suggestion, not evidence.Same method familySpatial Error Modelmachine-suggested · Relational suggestion, not evidence.Same method familySpatial Lag Modelmachine-suggested · Relational suggestion, not evidence.