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Fairness Implications of Heterogeneous Treatment Effect Estimation with
  Machine Learning Methods in Policy-making

Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making

2 September 2023
Patrick Rehill
Nicholas Biddle
    SyDa
    CML
ArXivPDFHTML

Papers citing "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making"

3 / 3 papers shown
Title
Forests for Differences: Robust Causal Inference Beyond Parametric DiD
Forests for Differences: Robust Causal Inference Beyond Parametric DiD
Hugo Gobato Souto
Francisco Louzada Neto
9
0
0
14 May 2025
Transparency challenges in policy evaluation with causal machine
  learning -- improving usability and accountability
Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability
Patrick Rehill
Nicholas Biddle
CML
ELM
24
3
0
20 Oct 2023
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
320
4,203
0
23 Aug 2019
1