Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …

A fundamental task in various disciplines of science, including biology, is to find underlying
causal relations and make use of them. Causal relations can be seen if interventions are …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Causal discovery aims at revealing causal relations from observational data, which is a
fundamental task in science and engineering. We describe causal-learn, an open-source …

Sudden, large-scale and diffuse human migration can amplify localized outbreaks of
disease into widespread epidemics,,–. Rapid and accurate tracking of aggregate population …

We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …

The machine learning community's interest in causality has significantly increased in recent
years. My understanding of causality has been shaped by Judea Pearl and a number of …

The dynamics and influence of fake news on Twitter during the 2016 US presidential
election remains to be clarified. Here, we use a dataset of 171 million tweets in the five …

Spatial transcriptomic studies are becoming increasingly common and large, posing
important statistical and computational challenges for many analytic tasks. Here, we present …

This work considers the question of how convenient access to copious data impacts our
ability to learn causal effects and relations. In what ways is learning causality in the era of …