Bandingkan metode

	Estimator Pencocokan	Pencocokan Tepat yang Dikasarankan (CEM)
Bidang	Inferensi Kausal	Inferensi Kausal
Keluarga	Regression model	Regression model
Tahun asal	1973	2011-2012
Pencetus	Rubin (1973); large-sample theory by Abadie & Imbens (2006)	Iacus, King, & Porro
Tipe	Nonparametric matching / causal inference	Matching / causal inference
Sumber perintis	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	nearest-neighbor matching, NNM, matching on covariates, covariate matching	CEM, coarsened matching, monotonic imbalance bounding matching
Terkait	6	6
Ringkasan	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.	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.