[Sds-seminars] [Sds-announce] SDS Talk by Ashesh Rambachan, 2/7 4pm "Identifying Prediction Mistakes in Observational Data"

[Dan Spielman]

ASHESH RAMBACHAN, Harvard University
Title: Identifying Prediction Mistakes in Observational Data
Monday, February 07, 20224:00PM to 5:00PM
Via Zoom: https://yale.zoom.us/j/91718936837
Website <https://scholar.harvard.edu/asheshr>
Information and 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 empirical settings such
as hiring, pretrial release, and medical testing. I derive a statistical
test for whether the decision maker makes systematic prediction mistakes
under these assumptions and show how supervised machine learning based
models can be used to apply this test. I provide methods for conducting
inference on the ways in which the decision maker’s predictions are
systematically biased. As an illustration, I apply this econometric
framework to analyze the pretrial release decisions of judges in New York
City, and I estimate that at least 20% of judges make systematic prediction
mistakes about failure to appear risk given defendant characteristics.*
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