Sammenlign metoder

	Robust Propensity Score Weighting	Vægtning med den inverse behandlingssandsynlighed (IPW / IPTW)
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	1994–2019	2000
Ophavsperson	Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW)	Robins, Hernán & Brumback
Type	Robust causal weighting estimator	Causal inference weighting estimator
Oprindelig kilde	Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Aliasser	robust PSW, robust IPW, robustness-augmented propensity score weighting, misspecification-robust weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relaterede	6	5
Resumé	Robust Propensity Score Weighting extends standard inverse probability weighting by incorporating safeguards against misspecification of the propensity score model and extreme weights. It combines techniques such as weight trimming, overlap weighting, or augmented outcome models to ensure that causal effect estimates remain reliable even when the propensity score model is imperfectly specified.	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.