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| Съвпадане по показател на склонност в образователни изследвания× | Претегляне с обратна вероятност на лечението (IPW / IPTW)× | |
|---|---|---|
| Област | Причинно-следствено заключение | Причинно-следствено заключение |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1983 (foundational); education adoption widespread from late 1990s | 2000 |
| Създател≠ | Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002) | Robins, Hernán & Brumback |
| Тип≠ | Quasi-experimental / matching-based causal inference | Causal inference weighting estimator |
| Основополагащ източник≠ | 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 ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Други названия≠ | PSM in education, educational PSM, PSM for program evaluation in schools, propensity matching education | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Свързани | 5 | 5 |
| Резюме≠ | Propensity Score Matching (PSM) in education research is a quasi-experimental technique that creates comparable treatment and control groups from observational student, teacher, or school data. By balancing groups on observed background characteristics, it enables credible causal estimates of educational interventions — such as tutoring programs, school choice policies, or teacher professional development — when random assignment is infeasible. | 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. |
| ScholarGateНабор от данни ↗ |
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