Efficient Symbolic Computations for Identifying Causal Effects
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914498842984448 |
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| author | Hollering, Benjamin Misra, Pratik Sturma, Nils |
| author_facet | Hollering, Benjamin Misra, Pratik Sturma, Nils |
| contents | Determining identifiability of causal effects from observational data under latent confounding is a central challenge in causal inference. For linear structural causal models, identifiability of causal effects is decidable through symbolic computation. However, standard approaches based on Gröbner bases become computationally infeasible beyond small settings due to their doubly exponential complexity. In this work, we study how to practically use symbolic computation for deciding rational identifiability. In particular, we present an efficient algorithm that provably finds the lowest degree identifying formulas. For a causal effect of interest, if there exists an identification formula of a prespecified maximal degree, our algorithm returns such a formula in quasi-polynomial time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20516 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Symbolic Computations for Identifying Causal Effects Hollering, Benjamin Misra, Pratik Sturma, Nils Machine Learning Determining identifiability of causal effects from observational data under latent confounding is a central challenge in causal inference. For linear structural causal models, identifiability of causal effects is decidable through symbolic computation. However, standard approaches based on Gröbner bases become computationally infeasible beyond small settings due to their doubly exponential complexity. In this work, we study how to practically use symbolic computation for deciding rational identifiability. In particular, we present an efficient algorithm that provably finds the lowest degree identifying formulas. For a causal effect of interest, if there exists an identification formula of a prespecified maximal degree, our algorithm returns such a formula in quasi-polynomial time. |
| title | Efficient Symbolic Computations for Identifying Causal Effects |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.20516 |