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Statistical analysis can be used as circumstantial evidence indicating that race, gender, age or other legally prohibited factor (exposure) was a significant cause of an unfavourable employment decision (response). Statistical methods, e.g. Cornfield's inequality, have been used to examine the employer's reason (omitted factor), e.g. requirement of having a professional certificate is able to explain the disparity. Based on a range of plausible values of the associations among omitted variable, exposure and response, the usual sensitivity analysis methods recalculate the  p -values of the test of association between exposure and response. However, the current methods treat the sensitivity parameters as fixed and do not account for sampling error. A new method of modelling an omitted factor is proposed for assessing the causal relationship between exposure and response. The association parameters between the omitted factor and the exposure are estimated using extra available data. The proposed method is flexible and can incorporate a wide range of statistical models. The method is illustrated using data from an age discrimination case.

Modelling an omitted factor in employment discrimination cases †