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 …

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

Identifying causal relationships and quantifying their strength from observational time series
data are key problems in disciplines dealing with complex dynamical systems such as the …

Complex systems are challenging to understand, especially when they defy manipulative
experiments for practical or ethical reasons. Several fields have developed parallel …

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

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 …

On time-series data, most causal discovery methods fit a new model whenever they
encounter samples from a new underlying causal graph. However, these samples often …

Causality is a fundamental notion in science, and plays an important role in explanation,
prediction, decision making and control. Recently, with the rapid accumulation of huge …

In addition to efficient statistical estimators of a treatment's effect, successful application of
causal inference requires specifying assumptions about the mechanisms underlying …

Discovering causal relationships between different variables from time series data has been
a long-standing challenge for many domains such as climate science, finance, and …