  We provide a new solution to the long-standing problem of inferring causality
from observations without modeling the unknown mechanisms. We show that the
evolution of any dynamical system is related to a predictive asymmetry that
quantifies causal connections from limited observations. A built-in
significance criterion obviates surrogate testing and drastically improves
computational efficiency. We validate our test on numerous synthetic systems
exhibiting behavior commonly occurring in nature, from linear and nonlinear
stochastic processes to systems exhibiting nonlinear deterministic chaos, and
on real-world data with known ground truths. Applied to the controversial
problem of glacial-interglacial sea level and CO$_{2}$ evolving in lock-step,
our test uncovers empirical evidence for CO$_{2}$ as a driver of sea level over
the last 800 thousand years. Our findings are relevant to any discipline where
time series are used to study natural systems.
