Generalizability in Causal Inference: Theory and Algorithms
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چکیده
of the Dissertation Generalizability in Causal Inference: Theory and Algorithms
منابع مشابه
An Introduction to Inference and Learning in Bayesian Networks
Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...
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The score reliability of language performance tests has attracted increasing interest. Classical Test Theory cannot examine multiple sources of measurement error. Generalizability theory extends Classical Test Theory to provide a practical framework to identify and estimate multiple factors contributing to the total variance of measurement. Generalizability theory by using analysis of variance ...
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Inferring causality is necessary to achieve the goal of epidemiology, which is to elucidate the cause of disease. Causal inference is conducted in three steps: evaluation of validity of the study, inference of general causality, and inference of individual causality. To evaluate validity of the study, we propose a checklist that focuses on biases and generalizability. For general causal inferen...
متن کاملComment: Generalizability theory and assessment in medical training.
Generalizability theory and assessment in medical training Even though it is more than 50 years old, generalizability theory, or G theory, is not well known to the medical community. It combines aspects of classic test theory and analysis of variance in order to estimate reliability (“G-study”) of a measurement instrument as a function of design aspects (“facets” in G-theory literature), and to...
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