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Bestie: Bayesian Estimation of Intervention Effects
An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.
Getting started
Browse package contents
Package details |
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| Author | Jack Kuipers [aut,cre] and Giusi Moffa [aut] |
| Maintainer | Jack Kuipers <jack.kuipers@bsse.ethz.ch> |
| License | GPL-3 |
| Version | 0.1.5 |
| Package repository | View on CRAN |
| Installation |
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