Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data

arXiv:2607.04527 · stat.ML, cs.LG, q-bio.GN · Submitted 2026-07-05 · Read on arXiv

Stephen Asiedu, David Watson

stat.ML, cs.LG, q-bio.GN

Submitted: 2026-07-05

Comments: Main material: 8 pages + supplementary material. 16 pages in all

Code: https://github.com/SparkAILab/Ascend

License: http://creativecommons.org/licenses/by/4.0/

The gist: Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects.

Terminology

Abstract

Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects. Although this ordering provides a natural scaffold for causal inference, most causal discovery and GRN methods either ignore the tiered organisation or condition on all upstream variables, which becomes infeasible for high-dimensional omics data. We present ASCEND (Ancestral Scalable Causal discovEry via iNherited Descent), a constraint-based framework that leverages known two-tiered structure to enable genome-scale causal discovery. ASCEND introduces a divide-and-conquer strategy that maintains dynamically updated ancestral conditioning sets for each downstream variable, dramatically reducing the number of conditional independence tests required, and achieves polynomial-time complexity where traditional approaches face exponential blow-up. Through extensive simulations and real biological data, we demonstrate that ASCEND accurately recovers ancestral relationships, scales properly and much faster, and outperforms existing gene regulatory network inference methods in both causal precision and computational efficiency. The algorithm's ability to resolve directionality makes it particularly suited for integrating multi-omic data where upstream regulators (e.g., SNPs, methylation sites) and downstream responses (e.g., gene expression) are measured jointly.

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