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

arXiv: https://arxiv.org/abs/2607.04527 · HTML: https://mlsanity.com/paper/2607.04527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 5 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data](https://mlsanity.com/paper/2210.17283/) (2022)
- [Ancestral Causal Inference](https://mlsanity.com/paper/1606.07035/) (2016)
- [Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View](https://mlsanity.com/paper/2403.15500/) (2024)
- [Causal Inference in Genetic Trio Studies](https://mlsanity.com/paper/2002.09644/) (2020)
- [Causal discovery under a confounder blanket](https://mlsanity.com/paper/2205.05715/) (2022)
- [Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery](https://mlsanity.com/paper/2304.03210/) (2023)
- [A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks](https://mlsanity.com/paper/1809.06827/) (2018)
- [Multi-omic Causal Discovery using Genotypes and Gene Expression](https://mlsanity.com/paper/2505.15866/) (2025)

## Abstract (arXiv)

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.