# Ancestral causal learning in high dimensions with a human genome-wide application

## TL;DR

This paper introduces a supervised learning method for inferring ancestral causal relationships in high-dimensional data, demonstrated on human genome-scale data with over 19,000 variables, showing robustness and scalability.

## Contribution

It presents a scalable, supervised approach to learn ancestral causal structures in high dimensions, leveraging known relationships and applying it to human genome data.

## Key findings

- Method is effective on human genome data with 19,000 variables.
- Results are robust to perturbations in prior knowledge.
- Approach scales to large, high-dimensional biological data.

## Abstract

We consider learning ancestral causal relationships in high dimensions. Our approach is driven by a supervised learning perspective, with discrete indicators of causal relationships treated as labels to be learned from available data. We focus on the setting in which some causal (ancestral) relationships are known (via background knowledge or experimental data) and put forward a general approach that scales to large problems. This is motivated by problems in human biology which are characterized by high dimensionality and potentially many latent variables. We present a case study involving interventional data from human cells with total dimension $p \! \sim \! 19{,}000$. Performance is assessed empirically by testing model output against previously unseen interventional data. The proposed approach is highly effective and demonstrably scalable to the human genome-wide setting. We consider sensitivity to background knowledge and find that results are robust to nontrivial perturbations of the input information. We consider also the case, relevant to some applications, where the only prior information available concerns a small number of known ancestral relationships.