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| Coarsened Exact Matching (CEM)× | Ważenie odwrotnością prawdopodobieństwa leczenia (IPW / IPTW)× | |
|---|---|---|
| Dziedzina | Wnioskowanie przyczynowe | Wnioskowanie przyczynowe |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2011-2012 | 2000 |
| Twórca≠ | Iacus, King, & Porro | Robins, Hernán & Brumback |
| Typ≠ | Matching / causal inference | Causal inference weighting estimator |
| Źródło pierwotne≠ | 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 ↗ |
| Inne nazwy≠ | CEM, coarsened matching, monotonic imbalance bounding matching | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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. |
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