Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data
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 downstr...
| Authors | Stephen Asiedu, David Watson |
|---|---|
| Primary category | stat.ML |
| Published | 5 July 2026 |
| Last updated at source | 10 July 2026 |
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