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| Propensity Score Matching× | Multiple Regressionsanalyse× | |
|---|---|---|
| Fachgebiet | Forschungsstatistik | Forschungsstatistik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1983 | 1801 |
| Urheber≠ | Paul Rosenbaum and Donald Rubin | Carl Friedrich Gauss |
| Typ | Method | Method |
| Wegweisende Quelle≠ | 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 ↗ | Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons. link ↗ |
| Aliasnamen | PSM, propensity score weighting, covariate balance | MLR, multivariate regression, linear regression |
| Verwandt≠ | 3 | 4 |
| Zusammenfassung≠ | 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. | Multiple regression analysis is a statistical method for modeling the relationship between a continuous dependent variable and two or more independent variables (predictors). Originating from Gauss's early 19th-century work and formalized by Draper and Smith (1966), it estimates linear equations predicting outcomes from multiple predictors while accounting for confounding relationships, making it indispensable in epidemiology, economics, psychology, and clinical research. |
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