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Differential Effects of Causality and Correlation on Inference

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A category is a set of entities associated by specific characteristics (features). These features can have different relations between one another, including correlations and causal connections. The purpose of this

A category is a set of entities associated by specific characteristics (features). These features can have different relations between one another, including correlations and causal connections. The purpose of this study was to examine how the relations between features would affect the inference of unknown features of new entities from a given set of features. Categories and their relations were learned in a Learning Phase, whereas features were inferred in Transfer and Selection Phases. Correct inference of feature was enhanced by correlation between the features given and the features inferred. It is less clear whether causal connections further enhanced correct inference of features over and above the effect of the correlation. Future research of this topic may benefit from utilizing more difficult tasks, repeating instructions, or manipulating the participants' understanding of the relation in ways other than administration of instructions.

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Date Created
  • 2013-05

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Visual analytics methodologies on causality analysis

Description

Causality analysis is the process of identifying cause-effect relationships among variables. This process is challenging because causal relationships cannot be tested solely based on statistical indicators as additional information is

Causality analysis is the process of identifying cause-effect relationships among variables. This process is challenging because causal relationships cannot be tested solely based on statistical indicators as additional information is always needed to reduce the ambiguity caused by factors beyond those covered by the statistical test. Traditionally, controlled experiments are carried out to identify causal relationships, but recently there is a growing interest in causality analysis with observational data due to the increasing availability of data and tools. This type of analysis will often involve automatic algorithms that extract causal relations from large amounts of data and rely on expert judgment to scrutinize and verify the relations. Over-reliance on these automatic algorithms is dangerous because models trained on observational data are susceptible to bias that can be difficult to spot even with expert oversight. Visualization has proven to be effective at bridging the gap between human experts and statistical models by enabling an interactive exploration and manipulation of the data and models. This thesis develops a visual analytics framework to support the interaction between human experts and automatic models in causality analysis. Three case studies were conducted to demonstrate the application of the visual analytics framework in which feature engineering, insight generation, correlation analysis, and causality inspections were showcased.

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Created

Date Created
  • 2019