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| Käänteisen todennäköisyyden painotus kasvatustieteellisessä tutkimuksessa× | Propensity Score Matching× | |
|---|---|---|
| Tieteenala≠ | Kausaalipäättely | Tutkimuksen tilastomenetelmät |
| Menetelmäperhe≠ | Regression model | Process / pipeline |
| Syntyvuosi≠ | 1983–2003 | 1983 |
| Kehittäjä≠ | Rosenbaum & Rubin (propensity score, 1983); Hirano, Imbens & Ridder (efficient IPW, 2003) | Paul Rosenbaum and Donald Rubin |
| Tyyppi≠ | Causal weighting estimator | Method |
| Alkuperäislähde≠ | Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189. 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 ↗ |
| Rinnakkaisnimet≠ | IPW in education, propensity-weighted analysis, IPTW education, inverse probability treatment weighting | PSM, propensity score weighting, covariate balance |
| Liittyvät≠ | 6 | 3 |
| Tiivistelmä≠ | Inverse Probability Weighting (IPW) is a causal inference technique that reweights observational education data to mimic a randomised experiment. Each student or school is assigned a weight equal to the inverse of the probability they received the treatment — thereby creating a pseudo-population in which programme participation is independent of measured background characteristics. The method is widely used in education research to evaluate school programmes, interventions, and policies from administrative or survey data. | 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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