Inference for Regression with Variables Generated by AI or Machine Learning
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908343315988480 |
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| author | Battaglia, Laura Christensen, Timothy Hansen, Stephen Sacher, Szymon |
| author_facet | Battaglia, Laura Christensen, Timothy Hansen, Stephen Sacher, Szymon |
| contents | Researchers now routinely use AI or other machine learning methods to estimate latent variables of economic interest, then plug-in the estimates as covariates in a regression. We show both theoretically and empirically that naively treating AI/ML-generated variables as "data" leads to biased estimates and invalid inference. To restore valid inference, we propose two methods: (1) an explicit bias correction with bias-corrected confidence intervals, and (2) joint estimation of the regression parameters and latent variables. We illustrate these ideas through applications involving label imputation, dimensionality reduction, and index construction via classification and aggregation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15585 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inference for Regression with Variables Generated by AI or Machine Learning Battaglia, Laura Christensen, Timothy Hansen, Stephen Sacher, Szymon Econometrics Machine Learning Researchers now routinely use AI or other machine learning methods to estimate latent variables of economic interest, then plug-in the estimates as covariates in a regression. We show both theoretically and empirically that naively treating AI/ML-generated variables as "data" leads to biased estimates and invalid inference. To restore valid inference, we propose two methods: (1) an explicit bias correction with bias-corrected confidence intervals, and (2) joint estimation of the regression parameters and latent variables. We illustrate these ideas through applications involving label imputation, dimensionality reduction, and index construction via classification and aggregation. |
| title | Inference for Regression with Variables Generated by AI or Machine Learning |
| topic | Econometrics Machine Learning |
| url | https://arxiv.org/abs/2402.15585 |