2019/10/19 by Kathpalia, Aditi, Nagaraj, Nithin
#Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME)
paper · doi:10.48550/arxiv.1910.08750
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 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.