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| Heterogen Behandlingseffekt Dobbelrobust Estimering× | Vægtning med den inverse behandlingssandsynlighed (IPW / IPTW)× | |
|---|---|---|
| Fagområde | Kausal inferens | Kausal inferens |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2018-2023 | 2000 |
| Ophavsperson≠ | Kennedy (2023); building on Robins, Rotnitzky & Zhao (1994) and Chernozhukov et al. (2018) | Robins, Hernán & Brumback |
| Type≠ | Semiparametric causal inference | Causal inference weighting estimator |
| Oprindelig kilde≠ | Kennedy, E. H. (2023). Towards optimal doubly robust estimation of heterogeneous causal effects. Electronic Journal of Statistics, 17(2), 3008-3049. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Aliasser≠ | DR-HTE, augmented IPW for HTE, doubly robust CATE estimation, semiparametric HTE estimation | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Relaterede | 5 | 5 |
| Resumé≠ | Doubly robust estimation of heterogeneous treatment effects (HTE) estimates how the causal effect of a treatment varies across subgroups or individual covariate values. By combining an outcome model and a propensity score model, it retains consistency if either model is correctly specified, and supports flexible machine learning nuisance estimators through cross-fitting to produce valid conditional average treatment effect (CATE) estimates. | 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. |
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