Linganisha mbinu

	Uzingatio wa Data ya Paneli wa Alama ya Mwelekeo	Uzito wa Alama ya Mwelekeo (PSW / IPW)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2000-2003	1983 (propensity score); 2003 (efficient IPW estimator)
Mwanzilishi	Hirano, Imbens & Ridder; Robins, Hernan & Brumback	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Aina	Causal inference / panel weighting	Causal inference / reweighting
Chanzo asilia	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	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.
Majina mbadala	panel PSW, panel IPW, longitudinal propensity score weighting, panel inverse probability weighting	PSW, inverse probability weighting, IPW, propensity-based weighting
Zinazohusiana	5	6
Muhtasari	Panel 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).