Included-Variable Bias and Everything but the Kitchen Sink

When estimating the risk of an adverse outcome, common statistical guidance is to include all available factors to maximize predictive performance. Similarly, in observational studies of discrimination, general practice is to adjust for all potential confounds to isolate any impermissible effect of legally protected traits, like race or gender, on decisions. I’ll argue that this popular "kitchen-sink” approach can in fact worsen predictions in the first case and yield misleading estimates of discrimination in the second. I’ll connect these results to ongoing debates in algorithmic fairness, criminal justice, healthcare, and college admissions.

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