Adversarial Debiasing for Unbiased Parameter Recovery
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915157312012288 |
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| author | Sanford, Luke C Ayers, Megan Gordon, Matthew Stone, Eliana |
| author_facet | Sanford, Luke C Ayers, Megan Gordon, Matthew Stone, Eliana |
| contents | Advances in machine learning and the increasing availability of high-dimensional data have led to the proliferation of social science research that uses the predictions of machine learning models as proxies for measures of human activity or environmental outcomes. However, prediction errors from machine learning models can lead to bias in the estimates of regression coefficients. In this paper, we show how this bias can arise, propose a test for detecting bias, and demonstrate the use of an adversarial machine learning algorithm in order to de-bias predictions. These methods are applicable to any setting where machine-learned predictions are the dependent variable in a regression. We conduct simulations and empirical exercises using ground truth and satellite data on forest cover in Africa. Using the predictions from a naive machine learning model leads to biased parameter estimates, while the predictions from the adversarial model recover the true coefficients. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12323 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adversarial Debiasing for Unbiased Parameter Recovery Sanford, Luke C Ayers, Megan Gordon, Matthew Stone, Eliana Machine Learning Advances in machine learning and the increasing availability of high-dimensional data have led to the proliferation of social science research that uses the predictions of machine learning models as proxies for measures of human activity or environmental outcomes. However, prediction errors from machine learning models can lead to bias in the estimates of regression coefficients. In this paper, we show how this bias can arise, propose a test for detecting bias, and demonstrate the use of an adversarial machine learning algorithm in order to de-bias predictions. These methods are applicable to any setting where machine-learned predictions are the dependent variable in a regression. We conduct simulations and empirical exercises using ground truth and satellite data on forest cover in Africa. Using the predictions from a naive machine learning model leads to biased parameter estimates, while the predictions from the adversarial model recover the true coefficients. |
| title | Adversarial Debiasing for Unbiased Parameter Recovery |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.12323 |