Сравнение на методи

	Машинно обучение-увеличено съвпадане по оценка на склонността	Прецизно съвпадение чрез окрупняване (CEM)
Област	Причинно-следствено заключение	Причинно-следствено заключение
Семейство	Regression model	Regression model
Година на възникване	2004	2011-2012
Създател	McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)	Iacus, King, & Porro
Тип	Causal inference / matching	Matching / causal inference
Основополагащ източник	McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4), 403-425.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Други названия	ML-PSM, boosted propensity score matching, ML-augmented PSM, nonparametric propensity score matching	CEM, coarsened matching, monotonic imbalance bounding matching
Свързани	6	6
Резюме	Machine learning-augmented propensity score matching (ML-PSM) replaces the traditional logistic regression used to estimate propensity scores with flexible machine learning algorithms — such as gradient boosted trees, random forests, or LASSO — to better capture complex, nonlinear relationships among covariates. The resulting richer propensity scores improve covariate balance and reduce bias in the estimated average treatment effect on the treated (ATT).	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.