Overcoming Preconceptions and Confirmation Biases Using Data Mining

Louis Anthony Cox ()
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Louis Anthony Cox: Cox Associates

Chapter Chapter 7 in Risk Analysis of Complex and Uncertain Systems, 2009, pp 179-202 from Springer

Abstract: Data-mining methods such as classification tree analysis, conditional independence tests, and causal graphs can be used to discover possible causal relations in data sets, even if the relations are unknown a priori and involve nonlinearities and high-order interactions. Chapter 6 showed that information theory provided one possible common framework and set of principles for applying these methods to support causal inferences. This chapter examines how to apply these methods and related statistical techniques (such as Bayesian model averaging) to empirically test preexisting causal hypotheses, either supporting them by showing that they are consistent with data, or refuting them by showing that they are not. In the latter case, data-mining and modeling methods can also suggest improved causal hypotheses.

Keywords: Conditional Independence; Bayesian Model Average; Causal Interpretation; Confirmation Bias; Bayesian Model Average (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/978-0-387-89014-2_7

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