Determining and measuring cause-effect relationships is fundamental to most scientific studies of natural phenomena. The notion of causation is distinctly different from correlation which only looks at the association of trends or patterns in measurements. In this article, we review different notions of causality and focus especially on measuring causality from time-series data. Causality testing finds numerous applications in diverse disciplines such as neuroscience, econometrics, climatology, physics, and artificial intelligence.