APO Models

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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.

DoubleML Version                    0.9.dev0
Script                   irm_apo_coverage.py
Date                     2024-08-16 10:42:18
Total Runtime (seconds)          5761.153235
Python Version                        3.12.4
Coverage for 95.0%-Confidence Interval over 1000 Repetitions
Learner g Learner m Treatment Level Bias CI Length Coverage
LGBM LGBM 0.000 2.076 10.316 0.964
LGBM LGBM 1.000 9.182 45.558 0.967
LGBM LGBM 2.000 9.607 44.674 0.952
LGBM Logistic 0.000 1.335 6.704 0.956
LGBM Logistic 1.000 1.697 8.845 0.968
LGBM Logistic 2.000 1.661 8.724 0.971
Linear LGBM 0.000 1.305 6.552 0.949
Linear LGBM 1.000 2.127 12.751 0.982
Linear LGBM 2.000 1.634 8.953 0.965
Linear Logistic 0.000 1.284 6.358 0.954
Linear Logistic 1.000 1.281 6.455 0.956
Linear Logistic 2.000 1.278 6.394 0.959
Coverage for 90.0%-Confidence Interval over 1000 Repetitions
Learner g Learner m Treatment Level Bias CI Length Coverage
LGBM LGBM 0.000 2.076 8.658 0.912
LGBM LGBM 1.000 9.182 38.233 0.915
LGBM LGBM 2.000 9.607 37.492 0.890
LGBM Logistic 0.000 1.335 5.626 0.904
LGBM Logistic 1.000 1.697 7.423 0.923
LGBM Logistic 2.000 1.661 7.321 0.917
Linear LGBM 0.000 1.305 5.498 0.901
Linear LGBM 1.000 2.127 10.701 0.949
Linear LGBM 2.000 1.634 7.514 0.931
Linear Logistic 0.000 1.284 5.336 0.902
Linear Logistic 1.000 1.281 5.418 0.904
Linear Logistic 2.000 1.278 5.366 0.907

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).

DoubleML Version                    0.9.dev0
Script                   irm_apo_coverage.py
Date                     2024-08-16 10:42:18
Total Runtime (seconds)          5761.153235
Python Version                        3.12.4
Coverage for 95.0%-Confidence Interval over 1000 Repetitions
Learner g Learner m Bias CI Length Coverage Uniform CI Length Uniform Coverage
LGBM LGBM 7.120 33.615 0.959 40.868 0.974
LGBM Logistic 1.571 8.090 0.962 9.592 0.961
Linear LGBM 1.725 9.418 0.967 11.234 0.975
Linear Logistic 1.279 6.402 0.955 6.818 0.953
Coverage for 90.0%-Confidence Interval over 1000 Repetitions
Learner g Learner m Bias CI Length Coverage Uniform CI Length Uniform Coverage
LGBM LGBM 7.120 28.210 0.906 36.194 0.924
LGBM Logistic 1.571 6.789 0.918 8.394 0.924
Linear LGBM 1.725 7.903 0.928 9.850 0.940
Linear Logistic 1.279 5.373 0.903 5.796 0.905

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).

DoubleML Version                    0.9.dev0
Script                   irm_apo_coverage.py
Date                     2024-08-16 10:42:18
Total Runtime (seconds)          5761.153235
Python Version                        3.12.4
Coverage for 95.0%-Confidence Interval over 1000 Repetitions
Learner g Learner m Bias CI Length Coverage Uniform CI Length Uniform Coverage
LGBM LGBM 9.787 45.131 0.949 51.424 0.965
LGBM Logistic 1.268 6.822 0.963 7.765 0.962
Linear LGBM 1.507 8.855 0.989 10.090 0.992
Linear Logistic 0.296 1.361 0.936 1.550 0.917
Coverage for 90.0%-Confidence Interval over 1000 Repetitions
Learner g Learner m Bias CI Length Coverage Uniform CI Length Uniform Coverage
LGBM LGBM 9.787 37.875 0.888 44.829 0.898
LGBM Logistic 1.268 5.725 0.927 6.774 0.927
Linear LGBM 1.507 7.431 0.958 8.799 0.975
Linear Logistic 0.296 1.143 0.873 1.351 0.872