Kathpalia, Aditi and Nagaraj, Nithin
(2019)
Data-based intervention approach for Complexity-Causality measure.
PeerJ Computer Science, 5:e196.
Full text not available from this repository.
| Abstract: |
Causality testing methods are being widely used in various disciplines of science. Model-free methods for causality estimation are very useful, as the underlying model generating the data is often unknown. However, existing model-free/data-driven measures assume separability of cause and effect at the level of individual samples of measurements and unlike model-based methods do not perform any intervention to learn causal relationships. These measures can thus only capture causality which is by the associational occurrence of ‘cause’ and ‘effect’ between well separated samples. In real-world processes, often ‘cause’ and ‘effect’ are inherently inseparable or become inseparable in the acquired measurements. We propose a novel measure that uses an adaptive interventional scheme to capture causality which is not merely associational. The scheme is based on characterizing complexities associated with the dynamical evolution of processes on short windows of measurements. The formulated measure, Compression-Complexity Causality is rigorously tested on simulated and real datasets and its performance is compared with that of existing measures such as Granger Causality and Transfer Entropy. The proposed measure is robust to the presence of noise, long-term memory, filtering and decimation, low temporal resolution (including aliasing), non-uniform sampling, finite length signals and presence of common driving variables. Our measure outperforms existing state-of-the-art measures, establishing itself as an effective tool for causality testing in real world applications. |
| Item Type: |
Journal Paper
|
| Keywords: |
Causality, Causal inference, Intervention, Compression-complexity, Model-based, Dynamical complexity, Negative causality |
| Subjects: |
School of Humanities > Consciousness Studies
Programmes > Consciousness Studies Programme > Measures of Causality |
| Divisions: |
Schools > Humanities |
| Date Deposited: |
23 Apr 2020 09:21 |
| Last Modified: |
23 Apr 2020 09:21 |
| Official URL: |
https://doi.org/10.7717/peerj-cs.196 |
| Related URLs: |
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| Funders: |
UNSPECIFIED |
| Projects: |
UNSPECIFIED |
| DOI: |
|
| URI: |
http://eprints.nias.res.in/id/eprint/1887 |
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