Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Méthodes de jumelage (CEM / Optimal / Génétique)× | Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)× | |
|---|---|---|
| Domaine | Inférence causale | Inférence causale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2012 | 2000 |
| Auteur d'origine≠ | Iacus, King & Porro (CEM); Hansen (optimal/full matching) | Robins, Hernán & Brumback |
| Type≠ | Matching for causal inference | Causal inference weighting estimator |
| Source fondatrice≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Alias | coarsened exact matching, optimal matching, genetic matching, CEM | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Apparentées | 5 | 5 |
| Résumé≠ | Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching. | 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. |
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