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 6 of 6 for “"Causal Structure Learning"”.
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Causal Structure Learning through Double Machine Learning
Learning the causal structure of a system solely from observational data is a fundamental yet intricate task with numerous applications across various fields, including economics, earth sciences, biology, and medicine. This task is challenging due to several reasons: i) observational data alone, as …
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Causal structure discovery from incomplete data
Causal structure learning is a fundamental tool for building a scientific understanding of the way a system works. However, in many application areas, such as genomics, the information necessary for current causal structure learning algorithms does not match the information that researchers can …
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Geometric Deep Learning for Healthcare Applications
… Networks (GNNs), a subset of Geometric Deep Learning methods, for medical image analysis and causal structure learning. Tracking the progression of pathologies in chest radiography poses several challenges in anatomical motion estimation and image registration as this task requires spatially …
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Causal discovery beyond Markov equivalence
The focus of the dissertation is on learning causal diagrams beyond Markov equivalence. The baseline assumptions in causal structure learning are the acyclicity of the underlying structure and causal sufficiency, which requires that there are no unobserved confounder variables in the system. Under …
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Fairness for affective and wellbeing computing
Recent advancements in machine learning (ML) as well as affective and wellbeing computing methodologies have enabled affective and wellbeing computing technologies to be increasingly used and integrated into daily human life. However, the problem of bias in machine-learning based tools and systems …
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Semiparametric Methods for Two Problems in Causal Inference using Machine Learning
… medicine require statistical guarantees on causal mechanisms, however in many settings only observational data with complex underlying interactions are available. Recent advances in machine learning have made it possible to model such systems, but their inherent biases and black-box nature …