Bayesian Hierarchical Invariant Prediction
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913002837508096 |
|---|---|
| author | Madaleno, Francisco Sand, Pernille Julie Viuff Pereira, Francisco C. Mejia, Sergio Hernan Garrido |
| author_facet | Madaleno, Francisco Sand, Pernille Julie Viuff Pereira, Francisco C. Mejia, Sergio Hernan Garrido |
| contents | We propose Bayesian Hierarchical Invariant Prediction (BHIP) reframing Invariant Causal Prediction (ICP) through the lens of Hierarchical Bayes. We leverage the hierarchical structure to explicitly test invariance of causal mechanisms under heterogeneous data, resulting in improved computational scalability for a larger number of predictors compared to ICP. Moreover, given its Bayesian nature BHIP enables the use of prior information. We evaluate BHIP on both synthetic and real-world datasets, demonstrating its potential as an alternative inference method to ICP and related methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Hierarchical Invariant Prediction Madaleno, Francisco Sand, Pernille Julie Viuff Pereira, Francisco C. Mejia, Sergio Hernan Garrido Machine Learning Artificial Intelligence Methodology We propose Bayesian Hierarchical Invariant Prediction (BHIP) reframing Invariant Causal Prediction (ICP) through the lens of Hierarchical Bayes. We leverage the hierarchical structure to explicitly test invariance of causal mechanisms under heterogeneous data, resulting in improved computational scalability for a larger number of predictors compared to ICP. Moreover, given its Bayesian nature BHIP enables the use of prior information. We evaluate BHIP on both synthetic and real-world datasets, demonstrating its potential as an alternative inference method to ICP and related methods. |
| title | Bayesian Hierarchical Invariant Prediction |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2505.11211 |