Dear Applied Statistics Workshop Community,
Welcome back! Our first meeting of the spring semester will be on January 24 (12:00 EST). Hans Demetrio Gaebler presents "Overcoming Statistical Challenges in Detecting Discrimination."
<When>
January 24, 12:00 to 1:30 PM, EST
Lunch will be available for pick-up inside CGIS K354.
<Where>
In-person: CGIS K354
Zoom:
https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09
<Abstract>
Outcome tests are a long-standing and widely used approach to detecting discrimination in lending, hiring, policing, and beyond. For example, if White loan recipients are found to default more often than racial minority recipients, the outcome test would suggest that lenders impose a double standard, preferentially lending to riskier White loan applicants. Despite its popularity, outcome tests have long been known to be statistically flawed, sometimes even suggesting discrimination against the group that in reality received preferential treatment. We propose two methods for remedying these statistical shortcomings. First, we show that a twist on standard outcome tests leads to surprisingly strong statistical guarantees. Our test is provably correct under a simple non-parametric assumption that we show — both empirically and theoretically — likely holds in many common scenarios. One limitation of this test is that it is, in some cases, inconclusive. In light of this, we introduce an alternative test of discrimination — which we call risk-adjusted regression — that can handle a broader range of cases, but which requires a richer set of covariates. This latter approach sheds light on the connection between statistical and legal understandings of discrimination.
<2023-2024 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
Best,
Jialu