Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Métodos de emparejamiento (CEM / Óptimo / Genético)× | Análisis de Sensibilidad para Sesgo Oculto (Rosenbaum Bounds / E-value)× | |
|---|---|---|
| Campo | Inferencia causal | Inferencia causal |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2012 | 2002 |
| Autor original≠ | Iacus, King & Porro (CEM); Hansen (optimal/full matching) | Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value) |
| Tipo≠ | Matching for causal inference | Sensitivity analysis for causal inference |
| Fuente seminal≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 |
| Alias | coarsened exact matching, optimal matching, genetic matching, CEM | Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. | Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017). |
| ScholarGateConjunto de datos ↗ |
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