Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866910287497527296 |
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| author | Guo, Zijian Zheng, Mengchu Bühlmann, Peter |
| author_facet | Guo, Zijian Zheng, Mengchu Bühlmann, Peter |
| contents | We discuss causal inference for observational studies with possibly invalid instrumental variables. We propose a novel methodology called two-stage curvature identification (TSCI) by exploring the nonlinear treatment model with machine learning. {The first-stage machine learning enables improving the instrumental variable's strength and adjusting for different forms of violating the instrumental variable assumptions.} The success of TSCI requires the instrumental variable's effect on treatment to differ from its violation form. A novel bias correction step is implemented to remove bias resulting from the potentially high complexity of machine learning. Our proposed \texttt{TSCI} estimator is shown to be asymptotically unbiased and Gaussian even if the machine learning algorithm does not consistently estimate the treatment model. Furthermore, we design a data-dependent method to choose the best among several candidate violation forms. We apply TSCI to study the effect of education on earnings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_12808 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning Guo, Zijian Zheng, Mengchu Bühlmann, Peter Methodology Statistics Theory Machine Learning We discuss causal inference for observational studies with possibly invalid instrumental variables. We propose a novel methodology called two-stage curvature identification (TSCI) by exploring the nonlinear treatment model with machine learning. {The first-stage machine learning enables improving the instrumental variable's strength and adjusting for different forms of violating the instrumental variable assumptions.} The success of TSCI requires the instrumental variable's effect on treatment to differ from its violation form. A novel bias correction step is implemented to remove bias resulting from the potentially high complexity of machine learning. Our proposed \texttt{TSCI} estimator is shown to be asymptotically unbiased and Gaussian even if the machine learning algorithm does not consistently estimate the treatment model. Furthermore, we design a data-dependent method to choose the best among several candidate violation forms. We apply TSCI to study the effect of education on earnings. |
| title | Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning |
| topic | Methodology Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2203.12808 |