Identifying Nonlinear Causal Relations in Large Data Sets

Louis Anthony Cox ()
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Louis Anthony Cox: Cox Associates

Chapter Chapter 6 in Risk Analysis of Complex and Uncertain Systems, 2009, pp 165-178 from Springer

Abstract: This chapter discusses data-mining methods for identifying potential causal relations in large data sets, such as clinical, epidemiological, or engineering reliability data sets. The causal relations to be discovered may be completely unknown initially; thus, successfully identifying them from data is sometimes called knowledge discovery. This is usually more challenging than merely estimating the parameters of a statistical model that is known or specified a priori. The causal relations may be complex and impossible to summarize using only a few parameters. For example, they may contain nonmonotonic (such as n-shaped or u-shaped) or threshold-like exposure-response relations, or more complicated nonlinearities, that render ineffective traditional statistical data analysis techniques (including factor analysis, principal components analysis, discriminant analysis, multiple linear or logistic regression, and so forth) based on linear and generalized linear modeling.

Date: 2009
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DOI: 10.1007/978-0-387-89014-2_6

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