Efficient Symbolic Computations for Identifying Causal Effects
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...
| Authors | Benjamin Hollering, Pratik Misra, Nils Sturma |
|---|---|
| Primary category | stat.ML |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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