  Advances in single-cell omics allow for unprecedented insights into the
transcription profiles of individual cells. When combined with large-scale
perturbation screens, through which specific biological mechanisms can be
targeted, these technologies allow for measuring the effect of targeted
perturbations on the whole transcriptome. These advances provide an opportunity
to better understand the causative role of genes in complex biological
processes such as gene regulation, disease progression or cellular development.
However, the high-dimensional nature of the data, coupled with the intricate
complexity of biological systems renders this task nontrivial. Within the
machine learning community, there has been a recent increase of interest in
causality, with a focus on adapting established causal techniques and
algorithms to handle high-dimensional data. In this perspective, we delineate
the application of these methodologies within the realm of single-cell genomics
and their challenges. We first present the model that underlies most of current
causal approaches to single-cell biology and discuss and challenge the
assumptions it entails from the biological point of view. We then identify open
problems in the application of causal approaches to single-cell data:
generalising to unseen environments, learning interpretable models, and
learning causal models of dynamics. For each problem, we discuss how various
research directions - including the development of computational approaches and
the adaptation of experimental protocols - may offer ways forward, or on the
contrary pose some difficulties. With the advent of single cell atlases and
increasing perturbation data, we expect causal models to become a crucial tool
for informed experimental design.

Machine Learning;Genomics