Many research questions in Earth and environmental sciences are inherently causal,
requiring robust analyses to establish whether and how changes in one variable cause …

Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

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 …

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 combinatorial problem of learning directed acyclic graphs (DAGs) from data was
recently framed as a purely continuous optimization problem by leveraging a differentiable …

Simulated DAG models may exhibit properties that, perhaps inadvertently, render their
structure identifiable and unexpectedly affect structure learning algorithms. Here, we show …

Learning graphical structure based on Directed Acyclic Graphs (DAGs) is a challenging
problem, partly owing to the large search space of possible graphs. A recent line of work …

Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …

Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses,
and ensuring the smooth operation and management of complex systems. Previous data …

Bayesian structure learning allows inferring Bayesian network structure from data while
reasoning about the epistemic uncertainty---a key element towards enabling active causal …