Identification and Debiased Learning of Causal Effects with General Instrumental Variables
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914313295364096 |
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| author | Chen, Shuyuan Zhang, Peng Cui, Yifan |
| author_facet | Chen, Shuyuan Zhang, Peng Cui, Yifan |
| contents | Instrumental variable methods are fundamental to causal inference when treatment assignment is confounded by unobserved variables. In this article, we develop a general nonparametric causal framework for identification and learning with multi-categorical or continuous instrumental variables. Specifically, the mean potential outcomes and the average treatment effect can be identified via a regular weighting function derived from the proposed framework. Leveraging semiparametric theory, we derive efficient influence functions and construct two consistent, asymptotically normal estimators via debiased machine learning. The first estimator uses a prespecified weighting function, while the second estimator selects the optimal weighting function adaptively. Extensions to longitudinal data, dynamic treatment regimes, and multiplicative instrumental variables are further developed. We demonstrate the proposed method by employing simulation studies and analyzing real data from the Job Training Partnership Act program. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20404 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identification and Debiased Learning of Causal Effects with General Instrumental Variables Chen, Shuyuan Zhang, Peng Cui, Yifan Methodology Econometrics Statistics Theory Machine Learning Instrumental variable methods are fundamental to causal inference when treatment assignment is confounded by unobserved variables. In this article, we develop a general nonparametric causal framework for identification and learning with multi-categorical or continuous instrumental variables. Specifically, the mean potential outcomes and the average treatment effect can be identified via a regular weighting function derived from the proposed framework. Leveraging semiparametric theory, we derive efficient influence functions and construct two consistent, asymptotically normal estimators via debiased machine learning. The first estimator uses a prespecified weighting function, while the second estimator selects the optimal weighting function adaptively. Extensions to longitudinal data, dynamic treatment regimes, and multiplicative instrumental variables are further developed. We demonstrate the proposed method by employing simulation studies and analyzing real data from the Job Training Partnership Act program. |
| title | Identification and Debiased Learning of Causal Effects with General Instrumental Variables |
| topic | Methodology Econometrics Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2510.20404 |