Linganisha mbinu

	Kikokotozi cha Kulinganisha Imara (Kurekebishwa kwa Upendeleo)	Ulinganifu kamili wa kukokotoa (CEM)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2006/2011	2011-2012
Mwanzilishi	Abadie & Imbens	Iacus, King, & Porro
Aina	Causal inference / matching	Matching / causal inference
Chanzo asilia	Abadie, A., & Imbens, G. W. (2011). Bias-Corrected Matching Estimators for Average Treatment Effects. Journal of Business & Economic Statistics, 29(1), 1-11.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Majina mbadala	bias-corrected matching, Abadie-Imbens matching, AI matching estimator, robust nearest-neighbor matching	CEM, coarsened matching, monotonic imbalance bounding matching
Zinazohusiana	6	6
Muhtasari	The robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alike. It yields consistent, asymptotically normal estimates of average treatment effects with a heteroskedasticity-robust variance formula that is valid regardless of the number of continuous covariates.	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.