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Bayesian Hierarchical Invariant Prediction

Created by
  • Haebom

Author

Francisco Madaleno, Pernille Julie Viuff Sand, Francisco C. Pereira, Sergio Hernan Garrido Mejia

Outline

Bayesian Hierarchical Invariant Prediction (BHIP) proposes a method to reformulate Invariant Causal Prediction (ICP) from the perspective of hierarchical Bayes. It improves computational scalability for more predictors than ICP by explicitly testing the invariance of causal mechanisms in heterogeneous data by leveraging the hierarchical structure. Furthermore, BHIP can use prior information due to its Bayesian property. In this paper, we test two sparsity inducing priors, horseshoe and spike-and-slab, to identify causal features more reliably. We demonstrate its potential as an alternative inference method to ICP by testing BHIP on synthetic and real data.

Takeaways, Limitations

Takeaways:
Provides improved computational scalability to handle more predictor variables than ICP.
The reliability of inference can be improved by utilizing prior information.
Identifying causal features more reliably through sparsity inducing priors.
Suggesting potential as an alternative inference method to ICP.
Limitations:
The paper lacks specific Limitations or constraints. Discussion of potential problems that may arise when applying to real data or further research directions is needed.
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