Frugal, Flexible, Faithful: Causal Data Simulation via Frengression
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909718432186368 |
|---|---|
| author | Yang, Linying Evans, Robin J. Shen, Xinwei |
| author_facet | Yang, Linying Evans, Robin J. Shen, Xinwei |
| contents | Machine learning has revitalized causal inference by combining flexible models and principled estimators, yet robust benchmarking and evaluation remain challenging with real-world data. In this work, we introduce frengression, a deep generative realization of the frugal parameterization that models the joint distribution of covariates, treatments and outcomes around the causal margin of interest. Frengression provides accurate estimation and flexible, faithful simulation of multivariate, time-varying data; it also enables direct sampling from user-specified interventional distributions. Model consistency and extrapolation guarantees are established, with validation on real-world clinical trial data demonstrating frengression's practical utility. We envision this framework sparking new research into generative approaches for causal margin modelling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01018 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Frugal, Flexible, Faithful: Causal Data Simulation via Frengression Yang, Linying Evans, Robin J. Shen, Xinwei Methodology Machine Learning Machine learning has revitalized causal inference by combining flexible models and principled estimators, yet robust benchmarking and evaluation remain challenging with real-world data. In this work, we introduce frengression, a deep generative realization of the frugal parameterization that models the joint distribution of covariates, treatments and outcomes around the causal margin of interest. Frengression provides accurate estimation and flexible, faithful simulation of multivariate, time-varying data; it also enables direct sampling from user-specified interventional distributions. Model consistency and extrapolation guarantees are established, with validation on real-world clinical trial data demonstrating frengression's practical utility. We envision this framework sparking new research into generative approaches for causal margin modelling. |
| title | Frugal, Flexible, Faithful: Causal Data Simulation via Frengression |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2508.01018 |