Optimal hyperparameter tuning of random forests for estimating causal treatment effects
Recent studies have expanded the focus of machine learning methods like random forests beyond prediction. They have found utility in the area of causal inference by using it to estimate propensity scores. It has also been established in the literature that tuning the hyperparameter values of rando...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Prince of Songkla University
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/66666c37aa284b68b145f52e104a4618 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Recent studies have expanded the focus of machine learning methods like random forests beyond prediction. They have
found utility in the area of causal inference by using it to estimate propensity scores. It has also been established in the literature
that tuning the hyperparameter values of random forests can improve the estimates of causal treatment effects. We thus address
the issue of getting the best out of random forest models by proposing to tune the random forest hyperparameters while
maximizing covariate balance. We consider variants of tuning based on a model fit criterion and compare with tuning to chase
covariate balance. In a simulation study and empirical application in two case studies, we studied the performance of different
tuning implementations, relative to the random forest with default hyperparameters. We find that tuning to chase balance rather
than model fit when estimating propensity scores induced better balance in the covariates and produced more accurate treatment
effect estimates. |
|---|