Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915321301958656 |
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| author | Yao, Shunxin |
| author_facet | Yao, Shunxin |
| contents | OC-DeepIV is a neural network model designed for estimating causal effects. It characterizes heterogeneity by adding interaction features and reduces redundancy through orthogonal constraints. The model includes two feature extractors, one for the instrumental variable Z and the other for the covariate X*. The training process is divided into two stages: the first stage uses the mean squared error (MSE) loss function, and the second stage incorporates orthogonal regularization. Experimental results show that this model outperforms DeepIV and DML in terms of accuracy and stability. Future research directions include applying the model to real-world problems and handling scenarios with multiple processing variables. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_02790 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation Yao, Shunxin Econometrics 62P20 I.2.6 OC-DeepIV is a neural network model designed for estimating causal effects. It characterizes heterogeneity by adding interaction features and reduces redundancy through orthogonal constraints. The model includes two feature extractors, one for the instrumental variable Z and the other for the covariate X*. The training process is divided into two stages: the first stage uses the mean squared error (MSE) loss function, and the second stage incorporates orthogonal regularization. Experimental results show that this model outperforms DeepIV and DML in terms of accuracy and stability. Future research directions include applying the model to real-world problems and handling scenarios with multiple processing variables. |
| title | Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation |
| topic | Econometrics 62P20 I.2.6 |
| url | https://arxiv.org/abs/2506.02790 |