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 3760 for “"Causal"”.
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Causal reconstruction : understanding causal descriptions of physical systems
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.
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Causal Programming
Causality is central to scientific inquiry. There is broad agreement on the meaning of causal statements, such as “Smoking causes cancer”, or, “Applying pesticides affects crop yields”. However, formalizing the intuition underlying such statements and conducting rigorous inference is difficult in …
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INVESTIGATING CAUSAL SELECTION: CAUSAL JUDGMENTS BETWEEN NORMS AND EXPECTATIONS
… both in philosophy and in cognitive science: causal selection. Experimental studies have shown that various factors—such as norms, agent knowledge, and the causal structure of the situation —affect people's attribution of different degrees of causality to events with the same dependency …
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Manifoldlike Causal Sets
… the important question of manifold likeness of causal sets. This problem has importance in the sense that in the continuum limit and in the case one finds a formalism for the sum over histories, the result requires to be embeddable in a manifold to be able to reproduce General Relativity. In …
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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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Causal Analysis Experiments on Log Extraction and Processing for Causal Insights
… systems do not necessarily inform users of the causal relationships that are inherent in the data. To this end, we design a new log-based data processing system that provides answers to causal questions based on timestamped logs. This thesis work focuses on improving the current log extraction …
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Seeing versus Doing: Causal Bayes Nets as Psychological Models of Causal Reasoning
Diese Dissertation geht der Frage nach, wie Menschen Vorhersagen über die Folgen von aktiven Interventionen in kausalen Systemen zu treffen, wenn sie diese Systeme zuvor nur passiv beobachtet haben. Die Theorie der kausalen Bayes-Netze (Spirtes, Glymour & Scheines, 1993; Pearl, 2000) stellt einen …
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Uniform FIR approximation of causal Wiener filters with applications to causal coherence
… of coherence is defined that captures the causal relationship between WSS processes. This causal coherence is interpreted in a modeling context and used to demonstrate what a frequency dependent measure for causality both can and can't represent. To understand how well frequency dependent …
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Causal Product Knowledge Management
… from experts and implement a novel web-based causal product design knowledge management system to systematically utilize the knowledge from experts, who are currently working or retired. The particular emphasis is on these research areas: 1) design knowledge acquisition, 2) causal knowledge …
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The Embodied Causal Learner
… 2008). For this specific study, the area of causal learning was examined.</p> <p>The primary goal of this specific study was to investigate whether elements of embodiment, and any mechanisms therein, would be found in the area of causal learning. That is, would motor actions irrelevant to …
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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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Evaluating the Role of Balanced Causal and Non-Causal Features in Predictive Modeling
… is motivated by recent work suggesting that causal features generalize better across domains as opposed to non-causal features. However, the evaluations comprised of varying sizes of input information across the models being compared. This study addresses this limitation by balancing the …
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From Logs to Causal Analysis: A Guided User Interface for Causal Graph Discovery
… in time, which can later be leveraged to answer causal questions about the system. However, analyzing logs is currently far from a smooth experience. Some system dynamics might only be partially captured by log variables, while others are drowned out by the sheer volume of uninteresting, …
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Quantitative models of causal judgment.
Quantitative models of causal judgment.
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Causal attributions for marital separation
… analyse the structure and content of real-life causal attributions, using this data to examine some important theoretical and empirical issues within attribution theory. Chapter one provides a justificatory backdrop to this research. It is argued here that this type of real-life exploratory …
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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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Causal Modeling of Organizational Commitment
… of organizational commitment by establishing a causal network among three individual characteristics--tenure, work motivation, and job satisfaction--two organizational/structural variables--decentralization and formalization,--and two job facets--the job characteristic model and job stress--as …
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Causal Relationships and Associative Formation
Made available in DSpace on 2014-12-10T21:08:00Z (GMT). No. of bitstreams: 1 7511827.pdf: 2701885 bytes, checksum: 0869face9cc5b3b8b11f781ff4040a77 (MD5) Previous issue date: 1974
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Learning and restructuring causal concepts
… this approach is that concepts are built on causal-explanatory knowledge, and hence, models of causal induction may help to clarify the mechanisms of the restructuring process. A new paradigm is presented to study the learning and revising of causal networks. Experiments 1 and 2 showed that …
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