Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Mètodes de parellatge (CEM / Òptim / Genètic)× | Emparellament per puntuació de propensió× | |
|---|---|---|
| Camp≠ | Inferència causal | Estadística per a la recerca |
| Família≠ | Regression model | Process / pipeline |
| Any d'origen≠ | 2012 | 1983 |
| Autor original≠ | Iacus, King & Porro (CEM); Hansen (optimal/full matching) | Paul Rosenbaum and Donald Rubin |
| Tipus≠ | Matching for causal inference | Method |
| Font 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., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Àlies≠ | coarsened exact matching, optimal matching, genetic matching, CEM | PSM, propensity score weighting, covariate balance |
| Relacionats≠ | 5 | 3 |
| Resum≠ | 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. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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