Causal Inference of Spectral Discontinuities using Gaussian Processes A Bayesian Non-parametric Method for Spectral Analysis in Quasi-Experimental Design
| dc.contributor.advisor | Hinne, Max | |
| dc.contributor.advisor | Heskes, Tom | |
| dc.contributor.author | Leeftink, David | |
| dc.date.issued | 2020-07-10 | |
| dc.description.abstract | Quasi-experimental designs are the next best inference technique when a randomized control trial is not available. Despite their popularity, common quasi-experimental frameworks have exclusively considered the time-domain representation of a signal. In this paper, we propose a new method for inferring spectral discontinuities in quasi-experimental designs. By using a Gaussian Process model with spectral kernels, a exible method for inferring discontinuities in the periodic features of a signal is obtained. To measure the average treatment e ect, two di erent e ect sizes are proposed. The consistency of the method is shown by applying it to simulated periodic data with a known discontinuity. Lastly, the method is applied to real-world examples by determining the e ect of Russia's 2006 alcohol policy on the monthly suicides, as well as inferring spectral discontinuities in a heart rate signal. | en_US |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/12679 | |
| dc.language.iso | en | en_US |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | en_US |
| dc.thesis.specialisation | Bachelor Artificial Intelligence | en_US |
| dc.thesis.studyprogramme | Artificial Intelligence | en_US |
| dc.thesis.type | Bachelor | en_US |
| dc.title | Causal Inference of Spectral Discontinuities using Gaussian Processes A Bayesian Non-parametric Method for Spectral Analysis in Quasi-Experimental Design | en_US |
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