Algebraic geometry of discrete interventional models
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Abstract (arXiv)
We investigate the algebra and geometry of general interventions in discrete DAG models. To this end, we introduce a theory for modeling soft interventions in the more general family of staged tree models and develop the formalism to study these models as parametrized subvarieties of a product of probability simplices. We then consider the problem of finding their defining equations, and we derive a combinatorial criterion for identifying interventional staged tree models for which the defining ideal is toric. We apply these results to the class of discrete interventional DAG models and establish a criteria to determine when these models are toric varieties.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Dec 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2021Context-Specific Causal Discovery for Categorical Data Using Staged Treescited by 10
- 2021Staged trees and asymmetry-labeled DAGscited by 8
- 2022Structural Learning of Simple Staged Treescited by 7
- 2022Highly Efficient Structural Learning of Sparse Staged Treescited by 6
- 2026Algebraic Signatures for Structural Learning in Probability Tensorscited by 0
- 2026Algebraic Signatures for Structural Learning in Probability Tensors21 Jul 2026
- 2022Highly Efficient Structural Learning of Sparse Staged Trees14 Jun 2022
- 2022Structural Learning of Simple Staged Trees8 Mar 2022
- 2021Staged trees and asymmetry-labeled DAGs4 Aug 2021
- 2021Context-Specific Causal Discovery for Categorical Data Using Staged Trees8 Jun 2021