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 422 for “"observational data"”.
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Machine Learning for causal Inference on Observational Data
… In contrast, it is most common to find Observational Data, in which the data that has been collected might be heavily unbalanced for treatment assignments, or the patients covariates might come from completely different distributions. Nevertheless, the ultimate goal of causal effects is …
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Separability of Effects in the Analysis of Complex Observational Data
Observational studies contribute to the overall body of knowledge, frequently capturing how medicine is practiced in a real-life setting, where patients and therapies are diverse and not limited by research protocol. However, they can present analytic challenges for these reasons, among them …
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Balance Optimization Subset Selection: a framework for causal inference with observational data
Observational data are prevalent in many fields of research, and it is desirable to use this data to explore potential causal relationships. Additional assumptions and methods for post-processing the data are needed to construct unbiased estimators of causal effects because such data is non-random. …
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Terrestrial Water Storage Change Over Dryland Regions Using Observational Data and Model Simulations
… the Tigris-Euphrates river basin by analyzing observational datasets and conducting model simulations. Over the basin, interannual climate variability appears to be a dominant contributor, followed by decadal climate change, while the impacts of climate intraannual variability on the declining …
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Impacts of Energy and Environmental Policies on Air Quality: Bridging Observational Data, Statistical, and Atmospheric Models
… actions. The increasing amount of measurement data on pollutant concentrations and precursor emissions provides an opportunity for tracking the progress of policies in mitigating air pollution, but key challenges remain. Levels of measured air pollutants and its precursor emissions are subject …
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Examining the Role of the Special Educator in a Response to Intervention Model
The purpose of this observational study was to examine the role of the special educator within a response-to-intervention (RTI) framework and to examine what instructional behaviors special educators evidence most frequently in the advanced RTI tiers. Specifically, these two issues were …
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Mistaking the Forest for the Trees: The Mistreatment of Group-level Treatments in the Study of American Politics
… of the limitations of and challenges in using observational data. Making causal inferences with observational data is difficult for numerous reasons. One reason is that one can never be sure that the estimate of interest is un-confounded by omitted variable bias (or, in causal terms, that a …
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Causal Structure Learning through Double Machine 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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Synthetic Data Generation and Sampling for Online Training of DNN in Manufacturing Supervised Learning Problems
… of Industrial Internet offers abundant passive data from manufacturing systems and networks, which enables data-driven modeling with high-data-demand, advanced statistical models such as Deep Neural Networks (DNNs). Deep Neural Networks (DNNs) have proven to be remarkably effective in supervised …
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Data-Rich Personalized Causal Inference
… level of personalization, or ethical concerns. Observational data offer a valuable alternative, but their lack of explicit randomization makes statistical analysis particularly challenging. In this thesis, we exploit the richness of modern observational data to develop methods for personalized …
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Inference in tough places : essays on modeling and matching with applications to civil conflict
… on the challenges of making inferences from observational data in the social sciences, with particular application to situations of violent conflict. The first essay utilizes quasi-experimental conditions to examine the effects of violence against civilians in Darfur, Sudan on attitudes …
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A Human Factors Approach for Identifying Latent Failures in Healthcare Settings
… look at accidents and incidents, for classifying observational data from various healthcare venues.</p> <p>METHOD: Three studies are presented to investigate the reliability of HFACS for classifying observational data. In Study I, HFACS was applied to observational human factors data collected …
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Mesoscale circulation in the Black Sea : a study combining numerical modelling and observations
… combining hydrodynamic numerical modelling with observational data analysis. Within this research: i) A folly working eddy resolving Black Sea model was set up using the POLCOMS 3-D numerical code, ii) The mesoscale circulation in the Black Sea during October-November 2004 was characterised from …
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Improved Communication for Safer Patient Care: The Implementation of SBAR
… and Quality (AHRQ) surveys, in addition to observational data, indicated a need for communication improvement. Lewin’s three step theory of change was applied in formulating an educational program to address current communication concerns, explore the benefits of the SBAR communication in an …
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Three algorithms for causal learning
… area involves the learning of structures from observational data alone and the second, involves the methodologies of conducting and learning from experiments. In this dissertation, I investigate three different aspects of causal learning, all of which are based on the causal Bayesian network …
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A tall tower wind investigation of northwest Missouri
… farms. Validation of current wind maps using observational data is of key importance because the observational data is actually coming from the heights at which wind turbines will operate. Diurnal variations in the wind fields were also studied, and thus far have shown that the area is capable …
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Robust and Data-Driven Uncertainty Quantification Methods as Real-Time Decision Support in Data-Driven Models
The growing complexity and data in modern engineering and physical systems require robust frameworks for real-time decision-making. Data-driven models trained on observational data enable faster predictions but face key challenges—data corruption, bias, limited interpretability, and uncertainty …
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Practical Methods for Scalable Bayesian and Causal Inference with Provable Quality Guarantees
… and a target response, from 𝑁 observed datapoints with 𝑁 ≪ 𝑝. For example, in genomics and precision medicine, there may be thousands or millions of genetic and environmental covariates but just hundreds or thousands of observed individuals. Researchers would like to (1) identify a small …
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Kinship and its consequences in the cooperatively breeding southern pied babbler Turdoides bicolor
… must be known. In this thesis, I use genetic and observational data to explore kinship between individuals in groups of wild Southern Pied Babblers Turdoides bicolor.
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Data science and advanced analytics : an integrated framework for creating value from data
… and customer management, can be addressed by data-driven modeling. Frequently, the only data available are observational rather than experimental. This precludes causal inference, though it supports quasi-causal inference (or causal approximation) and prediction. With three different studies …
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