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 …

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 …

Correlation is not causation: this simple and uncontroversial statement has far-reaching
implications. Defining and applying causality in biomedical research has posed significant …

We present a novel perspective and algorithm for learning directed acyclic graphs (DAGs)
from data generated by a linear structural equation model (SEM). First, we show that a linear …

We present a novel form of Fourier analysis, and associated signal processing concepts, for
signals (or data) indexed by edge-weighted directed acyclic graphs (DAGs). This means that …

Time-series causal discovery (TSCD) is a fundamental problem of machine learning.
However, existing synthetic datasets cannot properly evaluate or predict the algorithms' …

In drug discovery, mapping interactions between genes within cellular systems is a crucial
early step. This helps formulate hypotheses regarding molecular mechanisms that could …

The practical utility of causality in decision-making is widely recognized, with causal
discovery and inference being inherently intertwined. Nevertheless, a notable gap exists in …

We propose a method to detect model misspecifications in nonlinear causal additive and
potentially heteroscedastic noise models. We aim to identify predictor variables for which we …

Since distribution shifts are common in real-world applications, there is a pressing need for
developing prediction models that are robust against such shifts. Existing frameworks, such …