পদ্ধতির তুলনা করুন

	কার্যকারণ সম্পর্ক নির্ণয়ে স্থানিক সংবেদনশীলতা বিশ্লেষণ	প্রোপেনসিটি স্কোর ম্যাচিং (Propensity Score Matching)
ক্ষেত্র	কার্যকারণ অনুমান	গবেষণা পরিসংখ্যান
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	1988–2021 (developed progressively)	1983
প্রবর্তক	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Paul Rosenbaum and Donald Rubin
ধরন	Sensitivity / robustness analysis	Method
মৌলিক উৎস	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. ISBN: 978-9024737322	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
অপর নাম	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	PSM, propensity score weighting, covariate balance
সম্পর্কিত	6	3
সারসংক্ষেপ	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.