All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples
There is a long-standing debate in the statistical, epidemiological, and econometric fields as to whether nonparametric estimation that uses machine learning in model fitting confers any meaningful advantage over simpler, parametric approaches in finite sample estimation of causal effects. We addres...
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| Principais autores: | , , , , |
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| Format: | Artigo |
| Jezik: | Inglês |
| Izdano: |
De Gruyter
2023-11-01
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| Serija: | Journal of Causal Inference |
| Teme: | |
| Online dostop: | https://doi.org/10.1515/jci-2023-0022 |
| Oznake: |
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