विधियों की तुलना करें

	कार्य-कारणता के लिए स्थानिक संवेदनशीलता विश्लेषण	स्थानिक लैग मॉडल (SAR / स्थानिक ऑटोरिग्रेसिव)
क्षेत्र	कारणात्मक अनुमान	स्थानिक विश्लेषण
परिवार	Regression model	Regression model
उद्भव वर्ष	1988–2021 (developed progressively)	1988
प्रवर्तक	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Anselin (textbook formalisation); LeSage & Pace
प्रकार	Sensitivity / robustness analysis	Spatial autoregressive regression
मौलिक स्रोत	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. ISBN: 978-9024737322	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
उपनाम	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
संबंधित	6	5
सारांश	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.	The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.