# GATEs

## GATE Coverage

The simulations are based on the the make_heterogeneous_data-DGP with \(500\) observations. The groups are defined based on the first covariate, analogously to the GATE IRM Example, but rely on LightGBM to estimate nuisance elements (due to time constraints).

The non-uniform results (coverage, ci length and bias) refer to averaged values over all groups (point-wise confidende intervals).

Metadata

```
DoubleML Version 0.8.2
Script irm_gate_coverage.py
Date 2024-08-13 17:51:07
Total Runtime (seconds) 1738.824438
Python Version 3.12.4
```

Learner g | Learner m | Bias | CI Length | Coverage | Uniform CI Length | Uniform Coverage |
---|---|---|---|---|---|---|

LGBM | LGBM | 0.485 | 2.575 | 0.977 | 5.085 | 1.000 |

LGBM | Logistic Regression | 0.089 | 0.465 | 0.961 | 0.920 | 0.998 |

Lasso | LGBM | 0.497 | 2.440 | 0.959 | 4.820 | 1.000 |

Lasso | Logistic Regression | 0.090 | 0.478 | 0.960 | 0.940 | 0.999 |

Learner g | Learner m | Bias | CI Length | Coverage | Uniform CI Length | Uniform Coverage |
---|---|---|---|---|---|---|

LGBM | LGBM | 0.485 | 2.161 | 0.941 | 5.064 | 1.000 |

LGBM | Logistic Regression | 0.089 | 0.390 | 0.916 | 0.919 | 0.997 |

Lasso | LGBM | 0.497 | 2.048 | 0.904 | 4.807 | 1.000 |

Lasso | Logistic Regression | 0.090 | 0.401 | 0.917 | 0.942 | 0.999 |