Comparer des méthodes

	Spatial Coarsened Exact Matching (Spatial CEM)	Appariement par score de propension
Domaine	Inférence causale	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	2012 (CEM foundation); spatial extension in applied literature 2015-present	1983
Auteur d'origine	Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians	Paul Rosenbaum and Donald Rubin
Type	Quasi-experimental matching estimator with spatial covariates	Method
Source fondatrice	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	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.
Alias	Spatial CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates	PSM, propensity score weighting, covariate balance
Apparentées	6	3
Résumé	Spatial Coarsened Exact Matching applies the Coarsened Exact Matching framework to study designs involving geographic units — neighbourhoods, census tracts, municipalities, or grid cells. Covariates are coarsened into discrete bins and units are matched exactly on those bins, with spatial attributes (location, adjacency, geographic characteristics) incorporated as matching dimensions to control for spatial confounding.	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.