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| Μέθοδοι Αντιστοίχισης (CEM / Βέλτιστη / Γενετική)× | Αντιστοίχιση Βαθμολογίας Προδιάθεσης× | |
|---|---|---|
| Πεδίο≠ | Αιτιακή Συμπερασματολογία | Ερευνητική Στατιστική |
| Οικογένεια≠ | Regression model | Process / pipeline |
| Έτος προέλευσης≠ | 2012 | 1983 |
| Δημιουργός≠ | Iacus, King & Porro (CEM); Hansen (optimal/full matching) | Paul Rosenbaum and Donald Rubin |
| Τύπος≠ | Matching for causal inference | Method |
| Θεμελιώδης πηγή≠ | 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 ↗ |
| Εναλλακτικές ονομασίες≠ | coarsened exact matching, optimal matching, genetic matching, CEM | PSM, propensity score weighting, covariate balance |
| Συναφείς≠ | 5 | 3 |
| Σύνοψη≠ | 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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