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Overcoming Preconceptions and Confirmation Biases Using Data Mining

In: Risk Analysis of Complex and Uncertain Systems

Author

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  • Louis Anthony Cox

    (Cox Associates)

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.

Suggested Citation

  • Louis Anthony Cox, 2009. "Overcoming Preconceptions and Confirmation Biases Using Data Mining," International Series in Operations Research & Management Science, in: Risk Analysis of Complex and Uncertain Systems, chapter 0, pages 179-202, Springer.
    • Handle: RePEc:spr:isochp:978-0-387-89014-2_7
    DOI: 10.1007/978-0-387-89014-2_7
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