পদ্ধতির তুলনা করুন

	স্থানিক ম্যাচিং এস্টিমেটর	প্রোপেনসিটি স্কোর ম্যাচিং (Propensity Score Matching)
ক্ষেত্র	কার্যকারণ অনুমান	গবেষণা পরিসংখ্যান
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	2000s–2010s	1983
প্রবর্তক	Extension of Abadie & Imbens (2006) matching estimator to spatial settings; geographic applications developed in urban/environmental econometrics literature	Paul Rosenbaum and Donald Rubin
ধরন	Quasi-experimental causal inference	Method
মৌলিক উৎস	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.	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	geographic matching estimator, spatial nearest-neighbor matching, location-based matching estimator, spatially-weighted matching	PSM, propensity score weighting, covariate balance
সম্পর্কিত	6	3
সারসংক্ষেপ	The Spatial Matching Estimator estimates causal treatment effects by pairing each treated geographic unit with one or more similar untreated units nearby, exploiting the assumption that units close in space share similar unobserved characteristics. By restricting matches to a geographic neighbourhood or weighting by spatial proximity, the method controls for location-specific confounders that standard matching ignores.	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.