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| Metodi di Matching (CEM / Ottimale / Genetico)× | Inverse Probability of Treatment Weighting (IPW / IPTW)× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2012 | 2000 |
| Ideatore≠ | Iacus, King & Porro (CEM); Hansen (optimal/full matching) | Robins, Hernán & Brumback |
| Tipo≠ | Matching for causal inference | Causal inference weighting estimator |
| Fonte seminale≠ | 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 |
| Correlati | 5 | 5 |
| Sintesi≠ | 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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