Salīdzināt metodes

	Mašīnmācīšanās papildinātā raupjā precīzā saskaņošana (ML-CEM)	Coarsened Exact Matching (CEM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2012-2019	2011-2012
Autors	Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature	Iacus, King, & Porro
Tips	Matching / quasi-experimental	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	ML-augmented CEM, ML-CEM, automated coarsened exact matching, ML-assisted CEM	CEM, coarsened matching, monotonic imbalance bounding matching
Saistītās	6	6
Kopsavilkums	Machine Learning-Augmented Coarsened Exact Matching extends Coarsened Exact Matching (Iacus, King & Porro, 2012) by using supervised machine learning to automate and optimise the coarsening step — the discretisation of continuous covariates into bins — rather than relying on researcher-specified cutpoints. This reduces both ad hoc subjectivity in coarsening decisions and residual imbalance, while preserving CEM's core logic of exact matching within coarsened strata.	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.