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FADE: FAir Double Ensemble Learning for Observable and Counterfactual
  Outcomes

FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes

1 September 2021
Alan Mishler
Edward H. Kennedy
    FaML
ArXivPDFHTML

Papers citing "FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes"

5 / 5 papers shown
Title
Semiparametric Counterfactual Regression
Semiparametric Counterfactual Regression
Kwangho Kim
OffRL
31
0
0
03 Apr 2025
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy
  Learning
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
K. Kim
J. Zubizarreta
23
6
0
06 Jun 2023
Doubly Robust Counterfactual Classification
Doubly Robust Counterfactual Classification
K. Kim
Edward H. Kennedy
J. Zubizarreta
OffRL
33
5
0
15 Jan 2023
Learning Certifiably Optimal Rule Lists for Categorical Data
Learning Certifiably Optimal Rule Lists for Categorical Data
E. Angelino
Nicholas Larus-Stone
Daniel Alabi
Margo Seltzer
Cynthia Rudin
46
195
0
06 Apr 2017
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
201
2,082
0
24 Oct 2016
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