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 …

Many investigations into the world, including philosophical ones, aim to discover causal
knowledge, and many experimental methods have been developed to assist in causal …

We present constraint-based and (hybrid) score-based algorithms for causal structure
learning that estimate dynamic graphical models from multivariate time series data. In …

Maximum satisfiability (MaxSAT) is an optimization version of SAT that is solved by finding
an optimal truth assignment instead of just a satisfying one. In MaxSAT the objective function …

Understanding causal relationships is a central challenge in many research endeavours.
Recent research has shown the importance of accounting for feedback (cycles) and latent …

We are not only observers but also actors of reality. Our capability to intervene and alter the
course of some events in the space and time surrounding us is an essential component of …

Abstract Domain scientists interested in causal mechanisms are usually limited by the
frequency at which they can collect the measurements of social, physical, or biological …

Graphical structures estimated by causal learning algorithms from time series data can
provide highly misleading causal information if the causal timescale of the generating …

Detection of a causal relationship between two or more sets of data is an important problem
across various scientific disciplines. The Granger causality index and its derivatives are …

Many approaches to evidence amalgamation focus on relatively static information or
evidence: the data to be amalgamated involve different variables, contexts, or experiments …