Description Usage Arguments Details Value Examples

Helper function that dispatches to random forest (grf) for the post-double estimation.

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`X` |
A matrix of covariates (must be all numeric) |

`Y` |
A vector of the target variable, of same length as the number of rows of Y, must be numeric |

`W` |
A vector of the treatment variable, of same length as the number of rows of X, must be numeric |

`orthog.boost` |
Whether to use orthogonal boosting, defaults to FALSE. |

`tree.n` |
Controls the number of trees grown, defaults to 2000. |

`tune` |
Whether to use hyperparameter tuning. |

`clustered` |
Whether to use cluster robust forests, defaults to FALSE. |

If you need to use something that is not a default argument here, please refer to custom_generator. Not using honesty is heavily advised, though, as that could lead to a very high splitting, and the honesty is used for essentialy the same reason as crossfitting.

A list with two elements: The fitted W model and the fitted Y model.

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