手法を比較

	ロバスト逆確率重み付け (Robust IPW)	逆確率重み付け法 (IPW / IPTW)
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	2000-2004	2000
提唱者	Lunceford & Davidian (2004); Robins, Hernán & Brumback (2000)	Robins, Hernán & Brumback
種類	Causal weighting estimator	Causal inference weighting estimator
原典	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.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
別名	Robust IPW, Stabilized IPW, Trimmed IPW, Variance-robust IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
関連	5	5
概要	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.	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.