Biases in bias elicitation

Giancarlo Manzi and Martin Forster

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 18, 4656-4674

Abstract: We consider the biases that can arise in bias elicitation when expert assessors make random errors. After presenting a general framework of the phenomenon, we illustrate it for two examples: the case of omitting variables bias and that of the bias arising in adjusting relative risks. Results show that, even when assessors’ elicitations of bias have desirable properties, the nonlinear nature of biases can lead to elicitations of bias that are, themselves, biased. We show the corrections which can be made to remove this bias and discuss the implications for the applied literature which employs these methods.

Date: 2019
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DOI: 10.1080/03610926.2018.1500598

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