Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916661418786816 |
|---|---|
| author | Khellaf, Rémi Bellet, Aurélien Josse, Julie |
| author_facet | Khellaf, Rémi Bellet, Aurélien Josse, Julie |
| contents | We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) estimators derived from the Plug-in G-Formula, ranging from simple meta-analysis to one-shot and multi-shot federated learning, the latter leveraging the full data to learn the outcome model (albeit requiring more communication). Focusing on Randomized Controlled Trials (RCTs), we derive the asymptotic variance of these estimators for linear models. Our results provide practical guidance on selecting the appropriate estimator for various scenarios, including heterogeneity in sample sizes, covariate distributions, treatment assignment schemes, and center effects. We validate these findings with a simulation study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16870 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis Khellaf, Rémi Bellet, Aurélien Josse, Julie Machine Learning Statistics Theory We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) estimators derived from the Plug-in G-Formula, ranging from simple meta-analysis to one-shot and multi-shot federated learning, the latter leveraging the full data to learn the outcome model (albeit requiring more communication). Focusing on Randomized Controlled Trials (RCTs), we derive the asymptotic variance of these estimators for linear models. Our results provide practical guidance on selecting the appropriate estimator for various scenarios, including heterogeneity in sample sizes, covariate distributions, treatment assignment schemes, and center effects. We validate these findings with a simulation study. |
| title | Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2410.16870 |