In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …

There has been an increasing trend towards the use of complexity analysis in quantifying
neural activity measured by electroencephalography (EEG) signals. On top of revealing …

Causal inference in complex systems has been largely promoted by the proposal of some
advanced temporal causation models. However, temporal models have serious limitations …

Single-cell RNA sequencing (RNA-Seq) provides rich information about cell types and
states. However, it is difficult to capture rare dynamic processes, such as adult …

Identifying causal networks is important for effective policy and management
recommendations on climate, epidemiology, financial regulation, and much else. We …

It is often claimed that only experiments can support strong causal inferences and therefore
they should be privileged in the behavioral sciences. We disagree. Overvaluing experiments …

An important problem across many scientific fields is the identification of causal effects from
observational data alone. Recent methods (convergent cross map**, CCM) have made …

It is well known that current equilibrium-based models fall short as predictive descriptions of
natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For …

The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …

Ecological theory suggests that large-scale patterns such as community stability can be
influenced by changes in interspecific interactions that arise from the behavioural and/or …