Comparer des méthodes

	Spatial Coarsened Exact Matching (Spatial CEM)	Discontinuité de régression spatiale (Spatial RDD)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2012 (CEM foundation); spatial extension in applied literature 2015-present	2010s
Auteur d'origine	Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Type	Quasi-experimental matching estimator with spatial covariates	Quasi-experimental causal inference
Source fondatrice	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
Alias	Spatial CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Apparentées	6	4
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.	Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.