Estimating Conditional Average Treatment Effects with Heteroscedasticity by Model Averaging and Matching
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910746098532352 |
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| author | Shi, Pengfei Zhang, Xinyu Zhong, Wei |
| author_facet | Shi, Pengfei Zhang, Xinyu Zhong, Wei |
| contents | We propose a model averaging approach, combined with a partition and matching method to estimate the conditional average treatment effects under heteroskedastic error settings. The proposed approach has asymptotic optimality and consistency of weights and estimator. Numerical studies show that our method has good finite-sample performances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_06960 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Estimating Conditional Average Treatment Effects with Heteroscedasticity by Model Averaging and Matching Shi, Pengfei Zhang, Xinyu Zhong, Wei Methodology Applications We propose a model averaging approach, combined with a partition and matching method to estimate the conditional average treatment effects under heteroskedastic error settings. The proposed approach has asymptotic optimality and consistency of weights and estimator. Numerical studies show that our method has good finite-sample performances. |
| title | Estimating Conditional Average Treatment Effects with Heteroscedasticity by Model Averaging and Matching |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2304.06960 |