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 heart of the scientific enterprise is a rational effort to understand the causes behind the
phenomena we observe. In large-scale complex dynamical systems such as the Earth …

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 …

We introduce in this survey the major concepts, models, and algorithms proposed so far to
infer causal relations from observational time series, a task usually referred to as causal …

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 …

Intelligent agents should be able to learn useful representations by observing changes in
their environment. We model such observations as pairs of non-iid images sharing at least …

We propose to meta-learn causal structures based on how fast a learner adapts to new
distributions arising from sparse distributional changes, eg due to interventions, actions of …

Unsupervised domain adaptation is critical to many real-world applications where label
information is unavailable in the target domain. In general, without further assumptions, the …

Due to spurious correlations, machine learning systems often fail to generalize to
environments whose distributions differ from the ones used at training time. Prior work …

It is commonplace to encounter heterogeneous or nonstationary data, of which the
underlying generating process changes across domains or over time. Such a distribution …