Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Μοντέλο Οριακών Δομών για Αξιολόγηση Πολιτικής× | Αντίστροφη Πιθανότητα Στάθμισης Θεραπείας (IPW / IPTW)× | |
|---|---|---|
| Πεδίο | Αιτιακή Συμπερασματολογία | Αιτιακή Συμπερασματολογία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης | 2000 | 2000 |
| Δημιουργός≠ | James M. Robins, Miguel A. Hernan, Babette Brumback | Robins, Hernán & Brumback |
| Τύπος≠ | Causal inference / weighted regression | Causal inference weighting estimator |
| Θεμελιώδης πηγή≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Εναλλακτικές ονομασίες≠ | MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Συναφείς≠ | 6 | 5 |
| Σύνοψη≠ | A Policy Evaluation Marginal Structural Model (MSM) is a causal inference framework that estimates the population-average effect of a policy by using inverse probability weighting to create a pseudo-population in which treatment assignment is independent of measured confounders, enabling unbiased comparison of potential outcomes under different policy scenarios from observational data. | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
|
|