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Propensity Score Matching inom utbildningsforskning×Viktning med inversa sannolikheter för behandling (IPW / IPTW)×
ÄmnesområdeKausal inferensKausal inferens
FamiljRegression modelRegression model
Ursprungsår1983 (foundational); education adoption widespread from late 1990s2000
UpphovspersonRosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002)Robins, Hernán & Brumback
TypQuasi-experimental / matching-based causal inferenceCausal inference weighting estimator
UrsprungskällaRosenbaum, 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 ↗
AliasPSM in education, educational PSM, PSM for program evaluation in schools, propensity matching educationIPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Närliggande55
SammanfattningPropensity 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.
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ScholarGateJämför metoder: Propensity Score Matching in Education Research · Inverse Probability Weighting. Hämtad 2026-06-20 från https://scholargate.app/sv/compare