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Essays on Probabilistic Machine Learning for Economics 10.17863/CAM.78661 Kuhlen, Nikolas Carvalho, Vasco This thesis consists of three essays that explore the use of probabilistic machine learning techniques in combination with information-theoretic concepts to answer economic questions. Over the past years, economists have started applying machine learning methods to a wide range of topics. Probabilistic methods in the context of unsupervised learning represent one particular modelling approach at the intersection of computer science and statistics. While widely used in applied statistics, these models, however, do not necessarily provide relevant and interpretable outputs from an economist's perspective. In this thesis, I appeal to information-theoretic methods to summarise the probabilistic information inferred from such models and construct economically meaningful measures. University of Cambridge 2021-07-06 Thesis Doctoral Doctor of Philosophy (PhD) eng Nikolas Kuhlen gratefully acknowledges the financial support of The Alan Turing Institute under research award No. TU/C/000030. https://www.repository.cam.ac.uk/handle/1810/331216 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/c5747e60-80c9-408a-a809-a893f875c2c4/download cddbfa840d0a626c640e1c6997909397 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/0c58246f-d186-4c19-ad84-18995eddafb0/download 353adac0d1ebdfd65ab16480263c3c87 https://www.rioxx.net/licenses/all-rights-reserved/ Machine Learning Text Analysis News Information Theory Innovation Technology Patents