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| Model strukturalny brzegowy w badaniach edukacyjnych× | Dopasowanie wyników skłonności× | |
|---|---|---|
| Dziedzina≠ | Wnioskowanie przyczynowe | Statystyka w badaniach |
| Rodzina≠ | Regression model | Process / pipeline |
| Rok powstania≠ | 2000 (method); 2006 (canonical education application) | 1983 |
| Twórca≠ | James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application) | Paul Rosenbaum and Donald Rubin |
| Typ≠ | Causal inference / weighted regression model | Method |
| Źródło pierwotne≠ | Robins, J. M., Hernan, 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 ↗ |
| Inne nazwy≠ | MSM, marginal structural model, MSM with inverse probability weighting, IPW-MSM | PSM, propensity score weighting, covariate balance |
| Pokrewne≠ | 5 | 3 |
| Podsumowanie≠ | A marginal structural model (MSM) is a causal inference technique that uses inverse probability weighting to estimate the effect of a treatment or educational intervention that changes over time. Introduced by Robins, Hernán and Brumback (2000) in epidemiology and brought into education by Hong and Raudenbush (2006), MSMs handle time-varying confounding — a challenge that conventional regression cannot resolve. | 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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