Universität Bonn

Department of Economics

Ashesh Rambachan - Microsoft Research

"Identifying Prediction Mistakes in Observational Data"

Abstract

Decision makers, such as doctors, judges, and managers, make consequential choices based on predictions of unknown outcomes. Do these decision makers make systematic prediction mistakes based on the available information? If so, in what ways are their predictions systematically biased? Uncovering systematic prediction mistakes is difficult as the preferences and information sets of decision makers are unknown to researchers. In this paper, I characterize behavioral and econometric assumptions under which systematic prediction mistakes can be identified in observational empirical settings such as hiring, medical testing, and pretrial release. I derive a statistical test for whether the decision maker makes systematic prediction mistakes under these assumptions, and provide methods for conducting inference on the ways in which the decision maker’s predictions are systematically biased. As an empirical illustration, I analyze the pretrial release decisions of judges in New York City, estimating that at least 20% of judges make systematic prediction mistakes about failure to appear risk given defendant characteristics. Motivated by this behavioral analysis, I estimate the effects of replacing judges with algorithmic decision rules, and find that automating decisions where systematicprediction mistakes occur weakly dominates the status quo.

Additional information: