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| Inverse Probability of Treatment Weighting (IPW / IPTW)× | Regressione Logistica× | |
|---|---|---|
| Campo≠ | Inferenza causale | Statistica per la ricerca |
| Famiglia≠ | Regression model | Process / pipeline |
| Anno di origine≠ | 2000 | 1958 |
| Ideatore≠ | Robins, Hernán & Brumback | David Roxbee Cox |
| Tipo≠ | Causal inference weighting estimator | Method |
| Fonte seminale≠ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias≠ | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting | logit model, binomial logistic regression, LR |
| Correlati≠ | 5 | 3 |
| Sintesi≠ | 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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