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	Valutazione delle politiche tramite Coarsened Exact Matching (CEM)	Metodo del Controllo Sintetico (SCM)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	2011-2012	2003–2010
Ideatore	Iacus, King & Porro	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipo	Matching / quasi-experimental design	Quasi-experimental causal inference
Fonte seminale	Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
Alias	CEM, Coarsened Exact Matching, CEM policy evaluation, coarsening-based matching	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Correlati	5	4
Sintesi	Coarsened Exact Matching (CEM) is a quasi-experimental causal-inference technique that creates balanced treatment and control groups from observational data by temporarily coarsening covariates into bins, exactly matching units within those bins, and then pruning unmatched observations before estimating policy effects. Introduced by Iacus, King, and Porro, CEM belongs to the monotonic imbalance bounding family of matching methods and is especially popular in policy evaluation.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.