A Way to Synthetic Triple Difference
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912039245447168 |
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| author | Zhuang, Castiel Chen |
| author_facet | Zhuang, Castiel Chen |
| contents | This paper discusses a practical approach that combines synthetic control with triple difference to address violations of the parallel trends assumption. By transforming triple difference into a DID structure, we can apply synthetic control to a triple-difference framework, enabling more robust estimates when parallel trends are violated across multiple dimensions. The proposed procedure is applied to a real-world dataset to illustrate when and how we should apply this practice, while cautions are presented afterwards. This method contributes to improving causal inference in policy evaluations and offers a valuable tool for researchers dealing with heterogeneous treatment effects across subgroups. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_12353 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Way to Synthetic Triple Difference Zhuang, Castiel Chen Econometrics This paper discusses a practical approach that combines synthetic control with triple difference to address violations of the parallel trends assumption. By transforming triple difference into a DID structure, we can apply synthetic control to a triple-difference framework, enabling more robust estimates when parallel trends are violated across multiple dimensions. The proposed procedure is applied to a real-world dataset to illustrate when and how we should apply this practice, while cautions are presented afterwards. This method contributes to improving causal inference in policy evaluations and offers a valuable tool for researchers dealing with heterogeneous treatment effects across subgroups. |
| title | A Way to Synthetic Triple Difference |
| topic | Econometrics |
| url | https://arxiv.org/abs/2409.12353 |