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Padanan Skor Kecenderungan×Analisis Regresi Berganda×
BidangStatistik PenyelidikanStatistik Penyelidikan
KeluargaProcess / pipelineProcess / pipeline
Tahun asal19831801
PengasasPaul Rosenbaum and Donald RubinCarl Friedrich Gauss
JenisMethodMethod
Sumber perintisRosenbaum, 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 ↗Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons. link ↗
AliasPSM, propensity score weighting, covariate balanceMLR, multivariate regression, linear regression
Berkaitan34
RingkasanPropensity 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.Multiple regression analysis is a statistical method for modeling the relationship between a continuous dependent variable and two or more independent variables (predictors). Originating from Gauss's early 19th-century work and formalized by Draper and Smith (1966), it estimates linear equations predicting outcomes from multiple predictors while accounting for confounding relationships, making it indispensable in epidemiology, economics, psychology, and clinical research.
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ScholarGateBandingkan kaedah: Propensity Score Matching · Multiple Regression Analysis. Dicapai 2026-06-17 daripada https://scholargate.app/ms/compare