Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Hodnocení politik pomocí zhrublého exaktního párování (CEM)× | Vážená inverzní pravděpodobnost léčby (IPW / IPTW)× | |
|---|---|---|
| Obor | Kauzální inference | Kauzální inference |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 2011-2012 | 2000 |
| Tvůrce≠ | Iacus, King & Porro | Robins, Hernán & Brumback |
| Typ≠ | Matching / quasi-experimental design | Causal inference weighting estimator |
| Původní zdroj≠ | 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 ↗ |
| Další názvy≠ | CEM, Coarsened Exact Matching, CEM policy evaluation, coarsening-based matching | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | Coarsened Exact Matching (CEM) is a quasi-experimental causal-inference technique that creates balanced treatment and control groups from observational data by temporarily coarsening covariates into bins, exactly matching units within those bins, and then pruning unmatched observations before estimating policy effects. Introduced by Iacus, King, and Porro, CEM belongs to the monotonic imbalance bounding family of matching methods and is especially popular in policy evaluation. | 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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