Abstract
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.
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Acknowledgement
The authors gratefully acknowledge the financial support of ‘Cognitive Science Research Initiative’ (CSRI-DST) Grant No. DST/CSRI/2017/54(G) and Tata Trust provided for this research. Aditi Kathpalia is thankful to Manipal Academy of Higher Education for permitting this research as part of the PhD programme.
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Aditi Kathpalia is currently a PhD Scholar, Consciousness Studies Programme, National Institute of Advanced Studies, IISc Campus, Bengaluru. Her research interests include causality testing and its applications, chaos and information theory.
Nithin Nagaraj is currently Associate Professor, Consciousness Studies Programme, National Institute of Advanced Studies, IISc Campus, Bengaluru. His research areas include complexity theories of consciousness, chaos, information theory and causality testing.
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Kathpalia, A., Nagaraj, N. Measuring Causality. Reson 26, 191–210 (2021). https://doi.org/10.1007/s12045-021-1119-y
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DOI: https://doi.org/10.1007/s12045-021-1119-y