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| Instrumentális Változók az Oktatáskutatásban× | Tárgyhajlamossági pontszám illesztés× | |
|---|---|---|
| Tudományterület≠ | Oksági következtetés | Kutatási statisztika |
| Módszercsalád≠ | Regression model | Process / pipeline |
| Keletkezés éve≠ | 1991 (canonical education application) | 1983 |
| Megalkotó≠ | Angrist & Krueger (canonical 1991 education application); grounded in IV theory by Wright (1928) | Paul Rosenbaum and Donald Rubin |
| Típus≠ | Quasi-experimental causal identification | Method |
| Alapmű≠ | Angrist, J. D., & Krueger, A. B. (1991). Does Compulsory School Attendance Affect Schooling and Earnings? Quarterly Journal of Economics, 106(4), 979-1014. 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 ↗ |
| Alternatív nevek≠ | IV in education, 2SLS in education, education IV, school IV estimation | PSM, propensity score weighting, covariate balance |
| Kapcsolódó≠ | 5 | 3 |
| Összefoglaló≠ | Instrumental variables (IV) estimation is a quasi-experimental strategy for isolating the causal effect of schooling or educational interventions when assignment to treatment is confounded by unobserved factors. Pioneered in education economics by Angrist and Krueger's use of quarter-of-birth as an instrument for compulsory schooling, IV finds a source of exogenous variation in exposure to education and uses only that variation to estimate outcomes such as earnings, test scores, or attainment. | 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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