Causal Effect Identification and Inference with Endogenous Exposures and a Light-tailed Error
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912550228066304 |
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| author | Wang, Ruoyu Miao, Wang |
| author_facet | Wang, Ruoyu Miao, Wang |
| contents | Endogeneity poses significant challenges in causal inference across various research domains. This paper proposes a novel approach to identify and estimate causal effects in the presence of endogeneity. We consider a structural equation with endogenous exposures and an additive error term. Assuming the light-tailedness of the error term, we show that the causal effect can be identified by contrasting extreme conditional quantiles of the outcome given the exposures. Unlike many existing results, our identification approach does not rely on additional parametric assumptions or auxiliary variables. Building on the identification result, we develop a new method that estimates the causal effect using extreme quantile regression. We establish the consistency of the proposed extreme-based estimator under a general additive structural equation and demonstrate its asymptotic normality in the linear model setting. Simulations and data analysis of an automobile sale dataset show the effectiveness of our method in handling endogeneity. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_06211 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal Effect Identification and Inference with Endogenous Exposures and a Light-tailed Error Wang, Ruoyu Miao, Wang Methodology Endogeneity poses significant challenges in causal inference across various research domains. This paper proposes a novel approach to identify and estimate causal effects in the presence of endogeneity. We consider a structural equation with endogenous exposures and an additive error term. Assuming the light-tailedness of the error term, we show that the causal effect can be identified by contrasting extreme conditional quantiles of the outcome given the exposures. Unlike many existing results, our identification approach does not rely on additional parametric assumptions or auxiliary variables. Building on the identification result, we develop a new method that estimates the causal effect using extreme quantile regression. We establish the consistency of the proposed extreme-based estimator under a general additive structural equation and demonstrate its asymptotic normality in the linear model setting. Simulations and data analysis of an automobile sale dataset show the effectiveness of our method in handling endogeneity. |
| title | Causal Effect Identification and Inference with Endogenous Exposures and a Light-tailed Error |
| topic | Methodology |
| url | https://arxiv.org/abs/2408.06211 |