Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Variáveis Instrumentais Aumentadas por Machine Learning (ML-IV)× | Regressão Lasso× | |
|---|---|---|
| Área≠ | Inferência causal | Aprendizado de máquina |
| Família≠ | Regression model | Machine learning |
| Ano de origem≠ | 2012-2018 | 1996 |
| Autor original≠ | Belloni, Chernozhukov & Hansen; Chernozhukov et al. | Tibshirani, R. |
| Tipo≠ | Causal inference / semi-parametric estimation | Regularized linear regression (L1 penalty) |
| Fonte seminal≠ | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Outros nomes | ML-IV, MLIV, Double/Debiased ML with IV, DML-IV | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Relacionados | 4 | 4 |
| Resumo≠ | Machine learning-augmented instrumental variables combines the causal identification power of classical IV with modern high-dimensional machine learning — using methods such as LASSO, random forests, or neural networks to select valid instruments and model nuisance functions, thereby improving first-stage fit and enabling valid inference even when the number of potential instruments or controls is large relative to the sample size. | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. |
| ScholarGateConjunto de dados ↗ |
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