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 46 for “"causal structure"”.
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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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Causal structure of networks of stochastic processes
We propose different approaches to infer causal influences between agents in a network using only observed time series. This includes graphical models to depict causal relationships in the network, algorithms to identify the graphs in different scenarios and when only a subset of agents are …
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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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Learning models of environments with manifest causal structure
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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Does the Causal Structure of Space-Time Determine its Geometry
… to what extent is it possible to construe the causal structure of space-time as basic and from it to reconstruct the topological and metrical structure of space-time? The problem is first examined within the context of Minkowski space-time (Chapter II) and then generalized to the class of …
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A causal structure analysis of advertising effects on attitudes toward foreign brands
The present study examined the causal structure of advertising effects on consumers' attitude toward foreign brands in an advertising situation. The objectives of the study were to investigate effects of various factors regarding advertising, country perceptions and product on brand attitude, and …
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Information driven causal structure, evolution, and functionality of complex biosphere - atmosphere systems
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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Geometric Deep Learning for Healthcare Applications
… 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 aligning the sequential X-rays and modelling …
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Designing Better Scaffolding in Teaching Complex Systems with Graphical Simulations
… explanations); (2) understanding of the deep causal structure (i.e., being able to grasp and transfer the causal knowledge of a complex system). The study used a computer-based simulation environment as the research platform to teach the ideal gas law as a system. The ideal gas law is an …
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Advances in Latent Variable and Causal Models
… a framework is introduced to evaluate when two causal models are consistent with one another, meaning that a correspondence can be established between them such that reasoning about the effects of interventions in both models agree. This can be used to understand when two models of the same …
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Imaginative reasoning in probabilistic programs
… Specifically, humans understand the causal structure of the world, and mentally manipulate it to imagine worlds that could have been but were not, and even worlds that could never exist in reality. This thesis investigates computational principles of imaginative reasoning; develops …
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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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Latent Clustered Causal Models
… of latent variables. We define latent clustered causal models as a particular restriction on directed graphical models with latent variables and corresponding clusters of observed nodes, characterized by edges between only observed and latent variables. We discuss this model’s particular …
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Aspects of holography in Lorentz-violating gravity
… black hole thermodynamics rely on the standard causal structure of general relativity dictated by local light cones. It may therefore seem that the notion of holography is ultimately tied to the same causal structure, and hence, on the equivalence principle and local Lorentz …
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Emergent Times in Holographic Duality
… hole horizons, the interiors, and the associated causal structure. A key element is the emergence, in the large N limit of the boundary theory, of a type III1 von Neumann algebraic structure from the type I boundary operator algebra and the half-sided modular translation structure associated with …
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Learning to see the physical world
… world. The core idea is to exploit the generic, causal structure behind the world, including knowledge from computer graphics, physics, and language, in the form of approximate simulation engines, and to integrate them with deep learning.
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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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Horizons, hyperbolic systems, and inner boundary conditions in numerical relativity
… the event horizon (EH), which is the causal boundary separating the black hole interior from its exterior, in dynamical black hole spacetimes. In the EH studies, we formulate a set of tools for analyzing the behavior of the EH, including proposing a construction of the membrane …
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Information-Theoretic Algorithms and Identifiability for Causal Graph Discovery
… of widespread interest to learn the underlying causal structure for systems of random variables. Entropic Causal Inference is a recent framework for learning the causal graph between two variables from observational data (i.e., without experiments) by finding the information-theoretically …
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EVOLUTION AND THE TRANSFORMATION OF AMERICAN PHILOSOPHY
… science. They missed the radical change in the causal structure of science and philosophy implied by evolutionary philosophy. Later commentators on this period, with a few notable exceptions, have continued this trend. This has contributed to a disconnect of James, Peirce, and Dewey from the …
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