Comprehensively charting the biologically causal circuits that govern the phenotypic space
of human cells has often been viewed as an insurmountable challenge. However, in the last …

Causal learning is a key challenge in scientific artificial intelligence as it allows researchers
to go beyond purely correlative or predictive analyses towards learning underlying cause …

Highlights•Causal inference combines model and data to identify causations from
correlations.•Causality is indispensable for intervention, what if questions, and …

Latent variable models such as the Variational Auto-Encoder (VAE) have become a go-to
tool for analyzing biological data, especially in the field of single-cell genomics. One …

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 …

One of the grand challenges of cell biology is inferring the gene regulatory network (GRN)
which describes interactions between genes and their products that control gene expression …

Causal discovery in time-series is a fundamental problem in the machine learning
community, enabling causal reasoning and decision-making in complex scenarios …

Learning causal relationships between variables is a well-studied problem in statistics, with
many important applications in science. However, modeling real-world systems remain …

Causal inference is a vital aspect of multiple scientific disciplines and is routinely applied to
high-impact applications such as medicine. However, evaluating the performance of causal …

The effects of genetic variation on complex traits act mainly through changes in gene
regulation. Although many genetic variants have been linked to target genes in cis, the trans …