Comparer des méthodes

	Pondération par Probabilité Inverse dans la Recherche en Éducation	Coarsened Exact Matching (CEM)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1983–2003	2011-2012
Auteur d'origine	Rosenbaum & Rubin (propensity score, 1983); Hirano, Imbens & Ridder (efficient IPW, 2003)	Iacus, King, & Porro
Type	Causal weighting estimator	Matching / causal inference
Source fondatrice	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	IPW in education, propensity-weighted analysis, IPTW education, inverse probability treatment weighting	CEM, coarsened matching, monotonic imbalance bounding matching
Apparentées	6	6
Résumé	Inverse Probability Weighting (IPW) is a causal inference technique that reweights observational education data to mimic a randomised experiment. Each student or school is assigned a weight equal to the inverse of the probability they received the treatment — thereby creating a pseudo-population in which programme participation is independent of measured background characteristics. The method is widely used in education research to evaluate school programmes, interventions, and policies from administrative or survey data.	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.