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Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory
  and an Application to Racial Justice

Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice

16 October 2020
Andrii Babii
Xi Chen
Eric Ghysels
Rohit Kumar
    FaML
ArXivPDFHTML

Papers citing "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice"

3 / 3 papers shown
Title
Constrained Classification and Policy Learning
Constrained Classification and Policy Learning
T. Kitagawa
Shosei Sakaguchi
A. Tetenov
OffRL
24
12
0
24 Jun 2021
Fair prediction with disparate impact: A study of bias in recidivism
  prediction instruments
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
FaML
207
2,084
0
24 Oct 2016
High-dimensional generalized linear models and the lasso
High-dimensional generalized linear models and the lasso
Sara van de Geer
189
749
0
04 Apr 2008
1