Cargando…
Universal adaptability: Target-independent inference that competes with propensity scoring
The gold-standard approaches for gleaning statistically valid conclusions from data involve random sampling from the population. Collecting properly randomized data, however, can be challenging, so modern statistical methods, including propensity score reweighting, aim to enable valid inferences whe...
Autores principales: | Kim, Michael P., Kern, Christoph, Goldwasser, Shafi, Kreuter, Frauke, Reingold, Omer |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794832/ https://www.ncbi.nlm.nih.gov/pubmed/35046023 http://dx.doi.org/10.1073/pnas.2108097119 |
Ejemplares similares
-
Using Propensity Scores for Causal Inference: Pitfalls and Tips
por: Shiba, Koichiro, et al.
Publicado: (2021) -
Propensity‐score matching with competing risks in survival analysis
por: Austin, Peter C., et al.
Publicado: (2018) -
Commentary: Propensity score methods, causal inference, and hazard ratios
por: Andrei, Adin-Cristian
Publicado: (2021) -
Developing and Applying the Propensity Score to Make Causal Inferences: Variable Selection and Stratification
por: Adelson, Jill L., et al.
Publicado: (2017) -
From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making
por: Kuppler, Matthias, et al.
Publicado: (2022)