Salīdzināt metodes

	Spatial Coarsened Exact Matching (Spatial CEM)	Coarsened Exact Matching (CEM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2012 (CEM foundation); spatial extension in applied literature 2015-present	2011-2012
Autors	Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians	Iacus, King, & Porro
Tips	Quasi-experimental matching estimator with spatial covariates	Matching / causal inference
Pirmavots	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Citi nosaukumi	Spatial CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates	CEM, coarsened matching, monotonic imbalance bounding matching
Saistītās	6	6
Kopsavilkums	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.	Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.