Linganisha mbinu

	Uzito wa Inverse Probability (Robust IPW)	Uzito wa Alama ya Mwelekeo (PSW / IPW)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2000-2004	1983 (propensity score); 2003 (efficient IPW estimator)
Mwanzilishi	Lunceford & Davidian (2004); Robins, Hernán & Brumback (2000)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Aina	Causal weighting estimator	Causal inference / reweighting
Chanzo asilia	Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 23(19), 2937-2960.	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	Robust IPW, Stabilized IPW, Trimmed IPW, Variance-robust IPW	PSW, inverse probability weighting, IPW, propensity-based weighting
Zinazohusiana	5	6
Muhtasari	Robust Inverse Probability Weighting is a causal inference estimator that reweights observed units by stabilized or trimmed propensity score weights, then applies sandwich or bootstrap variance estimation to guard against model misspecification, extreme weights, and inflated standard errors. It extends standard IPW to improve finite-sample performance and inferential reliability in observational studies.	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).