@inproceedings{wang-etal-2021-enhancing,
title = "Enhancing Model Robustness and Fairness with Causality: A Regularization Approach",
author = "Wang, Zhao and
Shu, Kai and
Culotta, Aron",
editor = "Feder, Amir and
Keith, Katherine and
Manzoor, Emaad and
Pryzant, Reid and
Sridhar, Dhanya and
Wood-Doughty, Zach and
Eisenstein, Jacob and
Grimmer, Justin and
Reichart, Roi and
Roberts, Molly and
Shalit, Uri and
Stewart, Brandon and
Veitch, Victor and
Yang, Diyi",
booktitle = "Proceedings of the First Workshop on Causal Inference and NLP",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.cinlp-1.3/",
doi = "10.18653/v1/2021.cinlp-1.3",
pages = "33--43",
abstract = "Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features. Specifically, we first manually identify causal and spurious features with principles inspired from the counterfactual framework of causal inference. Then, we propose a regularization approach to penalize causal and spurious features separately. By adjusting the strength of the penalty for each type of feature, we build a predictive model that relies more on causal features and less on non-causal features. We conduct experiments to evaluate model robustness and fairness on three datasets with multiple metrics. Empirical results show that the new models built with causal awareness significantly improve model robustness with respect to counterfactual texts and model fairness with respect to sensitive attributes."
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<abstract>Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features. Specifically, we first manually identify causal and spurious features with principles inspired from the counterfactual framework of causal inference. Then, we propose a regularization approach to penalize causal and spurious features separately. By adjusting the strength of the penalty for each type of feature, we build a predictive model that relies more on causal features and less on non-causal features. We conduct experiments to evaluate model robustness and fairness on three datasets with multiple metrics. Empirical results show that the new models built with causal awareness significantly improve model robustness with respect to counterfactual texts and model fairness with respect to sensitive attributes.</abstract>
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%0 Conference Proceedings
%T Enhancing Model Robustness and Fairness with Causality: A Regularization Approach
%A Wang, Zhao
%A Shu, Kai
%A Culotta, Aron
%Y Feder, Amir
%Y Keith, Katherine
%Y Manzoor, Emaad
%Y Pryzant, Reid
%Y Sridhar, Dhanya
%Y Wood-Doughty, Zach
%Y Eisenstein, Jacob
%Y Grimmer, Justin
%Y Reichart, Roi
%Y Roberts, Molly
%Y Shalit, Uri
%Y Stewart, Brandon
%Y Veitch, Victor
%Y Yang, Diyi
%S Proceedings of the First Workshop on Causal Inference and NLP
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F wang-etal-2021-enhancing
%X Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features. Specifically, we first manually identify causal and spurious features with principles inspired from the counterfactual framework of causal inference. Then, we propose a regularization approach to penalize causal and spurious features separately. By adjusting the strength of the penalty for each type of feature, we build a predictive model that relies more on causal features and less on non-causal features. We conduct experiments to evaluate model robustness and fairness on three datasets with multiple metrics. Empirical results show that the new models built with causal awareness significantly improve model robustness with respect to counterfactual texts and model fairness with respect to sensitive attributes.
%R 10.18653/v1/2021.cinlp-1.3
%U https://aclanthology.org/2021.cinlp-1.3/
%U https://doi.org/10.18653/v1/2021.cinlp-1.3
%P 33-43
Markdown (Informal)
[Enhancing Model Robustness and Fairness with Causality: A Regularization Approach](https://aclanthology.org/2021.cinlp-1.3/) (Wang et al., CINLP 2021)
ACL