Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"Causal machine learning"”.
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Understanding ozone, climate and their interactions with causal machine learning
… outline why data-driven modelling, particularly machine learning, may address some weaknesses and describe relevant background on tropospheric ozone, climate projections and causal machine learning. In Chapter 3, we explore the trade-offs when building large deep learning models for ozone air …
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Causal Machine Learning to Discover Biochemical Determinants of Physical Fitness
… are likely to affect it. While contemporary deep learning models have demonstrated great success in pattern recognition and generation for various data modalities, their ability to decipher the causal mechanisms underlying these patterns is limited. This work proposes and evaluates a methodology …
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Essays in industrial organization
… largest online advertising platform and employs causal forest, a novel causal machine learning method, to estimate heterogeneous user click rates based on extensive browsing and ad exposure data from more than 1.6 million internet users. Simulations of user and platform behavior reveal that …
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Causal Inference and Evidence-Grounded Language Models for Trustworthy Personalized Clinical Decision Support
… in clinical reasoning. However, most machine learning approaches remain limited to risk prediction, lacking the causal reasoning, patient-specific personalization, and evidence-verifiable justification required for high-stakes medical decision-making. This thesis presents a principled …
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Essays on housing economics and household finance
… time using Generalized Random Forest (GRF), a causal machine learning model. At the county-level, the average elasticity ranges from 0.04 to 0.16 with some neighboring counties being up to eight standard deviations apart, while household elasticities range from 0.01 to 0.2. Among all …
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Improving Clinical Prediction Models with Statistical Representation Learning
<p>This dissertation studies novel statistical machine learning approaches for healthcare risk prediction applications in the presence of challenging scenarios, such as rare events, noisy observations, data imbalance, missingness and censoring. Such scenarios manifest frequently in practice, and …
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Three Essays on Machine Learning in Empirical Finance
… essays that contribute to the literature on machine learning in empirical finance. In the first paper, I create proxies for managers’ cultural fit using one of the latest machine learning technologies – the sentence embedding model - by analysing 11.5 million speeches in earnings calls. A …
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The Role of Explainable Artificial Intelligence in Data Science [Il Ruolo dell'Intelligenza Artificiale Esplicabile nella Scienza dei Dati]
… sanitario, dove la comprensione dei legami causali è fondamentale. In questo contesto, oltre a utilizzare metodi di interpretabilità, esploriamo l'inferenza causale in casi specifici, con l'obiettivo di discernere una possibile relazione tra XAI e inferenza causale. Un altro tipo di dato …