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 20 of 21 for “"Causal Discovery"”.
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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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Empirical evaluation of constraint-based and score-based causal discovery algorithms
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms
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Evaluating functional performance of evapotranspiration models based on causal discovery methods
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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On the finite sample complexity of causal discovery and the value of domain expertise
Causal discovery methods seek to identify causal relations between random variables from purely observational data, as opposed to actively collected experimental data where an experimenter intervenes on a subset of correlates. One of the seminal works in this area is the Inferred Causation (IC) …
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Causal Foundations for Pragmatic Data Science
A key goal of scientific discovery is the acquisition of knowledge that is practically useful for societal endeavors, such as the development of medicine or the design of fruitful economic policies. In this thesis, I place front and center the role that scientific models play in the process of …
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Causal Deep Learning with Applications in Healthcare
I am interested in inferring causality from observational data. Such causal inference is organised in two main categories: (i) causal discovery, and (ii) causal effect inference. With (i) we aim to determine whether or not a drug is causally responsible for an outcome; while (ii) already assumes …
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Toward Robust and Generalizable Spatiotemporal Modeling for Tasks beyond Forecasting and Classification
… anomaly detection, domain adaptation, and causal discovery. It systematically examines these issues across three cross-disciplinary application domains and proposes targeted, scenario-specific solutions: textbf{(1) Anomaly Detection:} We develop spatiotemporal anomaly detection frameworks …
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Learning Transferable Representations
A first contribution of this thesis is to propose causality as a language for problems of distribution shift. First, we consider domain generalisation, where no data from the test distribution are observed during training. What assumptions can be made regarding the relation between train and test …
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Minimax estimation with structured data : shape constraints, causal models, and optimal transport
… of statistics: shape-constrained estimation, causal discovery, and optimal transport. In the area of shape-constrained estimation, we study the estimation of matrices, first under the assumption of bounded total-variation (TV) and second under the assumption that the underlying matrix is …
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Causal Machine Learning to Discover Biochemical Determinants of Physical Fitness
… data modalities, their ability to decipher the causal mechanisms underlying these patterns is limited. This work proposes and evaluates a methodology using state-of-the-art causal discovery and causal inference methods to uncover the relationships between different proteins and their impact on …
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Causal and system-theoretic approaches to interpretable machine learning
… how it can be extended through tools from causal inference to reveal cause-effect relationships among input and output variables. We next consider Shapley values, another prominent XAI method that has demonstrated the ability to generate coherent explanations across different types of data, …
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Data-driven analytics to explore associations between risk and protective factors and school absenteeism for secondary school students
… at the local school district level. Applying causal discovery analysis techniques to student-level data, this study analyzed the interconnectivity of partial-day absence and full-day absence by comparing risk and protective factors operationalized by specific student-reported factors were …
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Hypothesis testing and causal inference with heterogeneous medical data
… the future is a fundamental part of scientific discovery. With increasingly heterogeneous data collection practices, exemplified by passively collected electronic health records or high-dimensional genetic data with only few observed samples, biases and spurious correlations are prevalent. These …
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Trends, problems, and solutions in causality and reinforcement learning
… and investigates the trends in the fields of causality and in reinforcement learning (RL). Theory is developed for both active research areas, with a specific focus on the overlap in underlying theory. The core argument is that the RL problem can be formulated as a causal problem, where the …
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Three algorithms for causal learning
The field of causal learning has grown in the past decade, establishing itself as a major focus in artificial intelligence research. Traditionally, approaches to causal learning are split into two areas. One area involves the learning of structures from observational data alone and the second, …
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Combining Functional and Automata Synthesis to Learn Causal Reactive Programs
… We focus on the particular domain of causal mechanism discovery in Atari-style grid worlds, and develop a synthesis algorithm that infers a program describing the causal rules of the world from a sequence of observations. We evaluate our algorithm on two benchmark datasets, including …
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Approaching Arctic-midlatitude dynamics from a two-way feedback perspective
… in this research is built around the ideas of causal discovery, particularly Granger causality. Most of these two-way Arctic-midlatitude relationships are considered in the context of added variance explained, or added predictive power. That is, these relationships are characterized by …
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Causal Graph Summarization
Causal inference is critical for scientific progress, especially in social sciences like public health and education—however, analysts often only have access to partial data which may lead to erroneous conclusions if critical confounding biases are not accounted for. To do this, they critically …
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Natural counterfactual explanations with causal awareness and actionable recourse for black-box models.
… infeasible for end-users, or neglect underlying causal relationships within the data, thereby limiting their real-world utility and trustworthiness. This thesis systematically addresses these critical limitations by developing and validating a multi-faceted framework for generating natural …
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Three Essays on Executive Incentive Pay and Causal Inference in Corporate Finance
… and firm outcome are complex and often the exact causal directions are ambiguous. Third, endogeneity is present and it’s likely many factors including CEO compensation and firm performance are jointly determined. Fourth, CEO compensation has been mainly characterized in a principal-agent framework …
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