قارن الطرق

	المطابقة الدقيقة المُغلّظة (CEM)	الترجيح الاحتمالي العكسي (IPW / IPTW)
المجال	الاستدلال السببي	الاستدلال السببي
العائلة	Regression model	Regression model
سنة النشأة	2011-2012	2000
صاحب الطريقة	Iacus, King, & Porro	Robins, Hernán & Brumback
النوع	Matching / causal inference	Causal inference weighting estimator
المصدر التأسيسي	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
الأسماء البديلة	CEM, coarsened matching, monotonic imbalance bounding matching	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
ذات صلة	6	5
الملخص	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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.