Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Vážená inverzní pravděpodobnost léčby (IPW / IPTW)× | Párování na základě skóre propensity× | |
|---|---|---|
| Obor≠ | Kauzální inference | Statistika ve výzkumu |
| Rodina≠ | Regression model | Process / pipeline |
| Rok vzniku≠ | 2000 | 1983 |
| Tvůrce≠ | Robins, Hernán & Brumback | Paul Rosenbaum and Donald Rubin |
| Typ≠ | Causal inference weighting estimator | Method |
| Původní zdroj≠ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. 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 ↗ |
| Další názvy≠ | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting | PSM, propensity score weighting, covariate balance |
| Příbuzné≠ | 5 | 3 |
| Shrnutí≠ | 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. | 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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