APO Models
APO Pointwise Coverage
The simulations are based on the the make_irm_data_discrete_treatments-DGP with \(500\) observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation.
Metadata
DoubleML Version 0.10.dev0
Script irm_apo_coverage.py
Date 2025-03-05 15:40:07
Total Runtime (seconds) 5746.482011
Python Version 3.12.9| Learner g | Learner m | Treatment Level | Bias | CI Length | Coverage |
|---|---|---|---|---|---|
| LGBM | LGBM | 0.000 | 2.077 | 10.316 | 0.966 |
| LGBM | LGBM | 1.000 | 9.216 | 45.558 | 0.968 |
| LGBM | LGBM | 2.000 | 9.580 | 44.674 | 0.952 |
| LGBM | Logistic | 0.000 | 1.339 | 6.704 | 0.954 |
| LGBM | Logistic | 1.000 | 1.693 | 8.845 | 0.969 |
| LGBM | Logistic | 2.000 | 1.661 | 8.724 | 0.969 |
| Linear | LGBM | 0.000 | 1.310 | 6.552 | 0.950 |
| Linear | LGBM | 1.000 | 2.129 | 12.751 | 0.983 |
| Linear | LGBM | 2.000 | 1.636 | 8.953 | 0.967 |
| Linear | Logistic | 0.000 | 1.289 | 6.358 | 0.953 |
| Linear | Logistic | 1.000 | 1.280 | 6.455 | 0.957 |
| Linear | Logistic | 2.000 | 1.285 | 6.394 | 0.957 |
| Learner g | Learner m | Treatment Level | Bias | CI Length | Coverage |
|---|---|---|---|---|---|
| LGBM | LGBM | 0.000 | 2.077 | 8.658 | 0.912 |
| LGBM | LGBM | 1.000 | 9.216 | 38.233 | 0.914 |
| LGBM | LGBM | 2.000 | 9.580 | 37.492 | 0.892 |
| LGBM | Logistic | 0.000 | 1.339 | 5.626 | 0.905 |
| LGBM | Logistic | 1.000 | 1.693 | 7.423 | 0.924 |
| LGBM | Logistic | 2.000 | 1.661 | 7.321 | 0.920 |
| Linear | LGBM | 0.000 | 1.310 | 5.498 | 0.900 |
| Linear | LGBM | 1.000 | 2.129 | 10.701 | 0.948 |
| Linear | LGBM | 2.000 | 1.636 | 7.514 | 0.933 |
| Linear | Logistic | 0.000 | 1.289 | 5.336 | 0.903 |
| Linear | Logistic | 1.000 | 1.280 | 5.418 | 0.909 |
| Linear | Logistic | 2.000 | 1.285 | 5.366 | 0.906 |
APOS Coverage
The simulations are based on the the make_irm_data_discrete_treatments-DGP with \(500\) observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation.
The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals).
Metadata
DoubleML Version 0.10.dev0
Script irm_apo_coverage.py
Date 2025-03-05 15:40:07
Total Runtime (seconds) 5746.482011
Python Version 3.12.9| Learner g | Learner m | Bias | CI Length | Coverage | Uniform CI Length | Uniform Coverage |
|---|---|---|---|---|---|---|
| LGBM | LGBM | 7.120 | 33.615 | 0.958 | 40.868 | 0.973 |
| LGBM | Logistic | 1.573 | 8.090 | 0.962 | 9.592 | 0.960 |
| Linear | LGBM | 1.729 | 9.418 | 0.968 | 11.234 | 0.974 |
| Linear | Logistic | 1.283 | 6.402 | 0.957 | 6.818 | 0.953 |
| Learner g | Learner m | Bias | CI Length | Coverage | Uniform CI Length | Uniform Coverage |
|---|---|---|---|---|---|---|
| LGBM | LGBM | 7.120 | 28.210 | 0.907 | 36.194 | 0.926 |
| LGBM | Logistic | 1.573 | 6.789 | 0.913 | 8.394 | 0.922 |
| Linear | LGBM | 1.729 | 7.903 | 0.927 | 9.850 | 0.936 |
| Linear | Logistic | 1.283 | 5.373 | 0.905 | 5.796 | 0.902 |
Causal Contrast Coverage
The simulations are based on the the make_irm_data_discrete_treatments-DGP with \(500\) observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation.
The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals).
Metadata
DoubleML Version 0.10.dev0
Script irm_apo_coverage.py
Date 2025-03-05 15:40:07
Total Runtime (seconds) 5746.482011
Python Version 3.12.9| Learner g | Learner m | Bias | CI Length | Coverage | Uniform CI Length | Uniform Coverage |
|---|---|---|---|---|---|---|
| LGBM | LGBM | 9.789 | 45.131 | 0.949 | 51.424 | 0.965 |
| LGBM | Logistic | 1.267 | 6.822 | 0.964 | 7.765 | 0.963 |
| Linear | LGBM | 1.506 | 8.855 | 0.989 | 10.090 | 0.992 |
| Linear | Logistic | 0.294 | 1.361 | 0.935 | 1.550 | 0.920 |
| Learner g | Learner m | Bias | CI Length | Coverage | Uniform CI Length | Uniform Coverage |
|---|---|---|---|---|---|---|
| LGBM | LGBM | 9.789 | 37.875 | 0.888 | 44.829 | 0.898 |
| LGBM | Logistic | 1.267 | 5.725 | 0.927 | 6.774 | 0.927 |
| Linear | LGBM | 1.506 | 7.431 | 0.958 | 8.799 | 0.975 |
| Linear | Logistic | 0.294 | 1.143 | 0.874 | 1.351 | 0.871 |