Linganisha mbinu

	Uzito wa Kinyume wa Uwezekano wa Matibabu (IPW / IPTW)	Ulinganishaji wa Alama ya Mwelekeo
Nyanja	Uhitimisho wa Kisababishi	Takwimu za Utafiti
Familia	Regression model	Process / pipeline
Mwaka wa asili	2000	1983
Mwanzilishi	Robins, Hernán & Brumback	Paul Rosenbaum and Donald Rubin
Aina	Causal inference weighting estimator	Method
Chanzo asilia	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.	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	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting	PSM, propensity score weighting, covariate balance
Zinazohusiana	5	3
Muhtasari	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.	Propensity 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.