Learning for Counterfactual Fairness from Observational Data

Author:

Ma Jing1ORCID,Guo Ruocheng2ORCID,Zhang Aidong1ORCID,Li Jundong1ORCID

Affiliation:

1. University of Virginia, Charlottesville, USA

2. Bytedance Research, London, United Kingdom

Funder

Commonwealth Cyber Initiative awards

4-VA collaborative research grant

JP Morgan Chase Faculty Research Award

UVA 3 Cavaliers seed grant

National Science Foundation

Cisco Faculty Research Award

Jefferson Lab subcontract

Publisher

ACM

Reference56 articles.

1. Carlos Aguirre , Keith Harrigian , and Mark Dredze . 2021. Gender and racial fairness in depression research using social media. arXiv preprint ( 2021 ). Carlos Aguirre, Keith Harrigian, and Mark Dredze. 2021. Gender and racial fairness in depression research using social media. arXiv preprint (2021).

2. Kartik Ahuja Karthikeyan Shanmugam Kush Varshney and Amit Dhurandhar. 2020. Invariant risk minimization games. In ICML. 145--155. Kartik Ahuja Karthikeyan Shanmugam Kush Varshney and Amit Dhurandhar. 2020. Invariant risk minimization games. In ICML. 145--155.

3. Martin Arjovsky , Léon Bottou , Ishaan Gulrajani , and David Lopez-Paz . 2019. Invariant risk minimization. arXiv preprint ( 2019 ). Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint (2019).

4. Solon Barocas , Moritz Hardt , and Arvind Narayanan . 2017. Fairness in machine learning. NeurIPS tutorial , Vol. 1 ( 2017 ), 2017. Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2017. Fairness in machine learning. NeurIPS tutorial , Vol. 1 (2017), 2017.

5. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

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