Imperial College London
Alternate

ProfessorMauricioBarahona

Faculty of Natural Sciences, Department of Mathematics

Director of Research, Chair in Biomathematics
 
 
 
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Contact

 

m.barahona Website

 
 
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Location

 

6M31Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Laumann:2023:10.3390/e25121597,
author = {Laumann, F and von, Kuegelgen J and Park, J and Scholkopf, B and Barahona, M},
doi = {10.3390/e25121597},
journal = {Entropy: international and interdisciplinary journal of entropy and information studies},
title = {Kernel-based independence tests for causal structure learning on functional data},
url = {http://dx.doi.org/10.3390/e25121597},
volume = {25},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert–Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert–Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data.
AU  - Laumann,F
AU  - von,Kuegelgen J
AU  - Park,J
AU  - Scholkopf,B
AU  - Barahona,M
DO  - 10.3390/e25121597
PY  - 2023///
SN  - 1099-4300
TI  - Kernel-based independence tests for causal structure learning on functional data
T2  - Entropy: international and interdisciplinary journal of entropy and information studies
UR  - http://dx.doi.org/10.3390/e25121597
UR  - http://hdl.handle.net/10044/1/108107
VL  - 25
ER  -
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