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Pembobotan Skor Kecenderungan Data Panel×Penimbang Skor Kecenderungan (PSW / IPW)×
BidangInferens KausalInferens Kausal
KeluargaRegression modelRegression model
Tahun asal2000-20031983 (propensity score); 2003 (efficient IPW estimator)
PengasasHirano, Imbens & Ridder; Robins, Hernan & BrumbackRosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
JenisCausal inference / panel weightingCausal inference / reweighting
Sumber perintisHirano, 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 ↗
Aliaspanel PSW, panel IPW, longitudinal propensity score weighting, panel inverse probability weightingPSW, inverse probability weighting, IPW, propensity-based weighting
Berkaitan56
RingkasanPanel Data Propensity Score Weighting (panel PSW) extends inverse probability weighting to longitudinal settings where the same units are observed across multiple time periods. It reweights observations by the inverse of each unit's time-varying probability of receiving treatment, creating a pseudo-population in which treatment is balanced on observed covariates at each period, and then estimates causal effects from repeated-measures data.Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).
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ScholarGateBandingkan kaedah: Panel Data Propensity Score Weighting · Propensity Score Weighting. Dicapai 2026-06-18 daripada https://scholargate.app/ms/compare