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.
NoteMetadata
DoubleML Version 0.12.dev0
Script APOCoverageSimulation
Date 2025-12-04 18:24
Total Runtime (minutes) 75.00107
Python Version 3.12.3
Config File scripts/irm/apo_config.yml
Loading ITables v2.5.2 from the init_notebook_mode cell...
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Loading ITables v2.5.2 from the init_notebook_mode cell...
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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 levels (point-wise confidende intervals).
NoteMetadata
DoubleML Version 0.12.dev0
Script APOSCoverageSimulation
Date 2025-12-04 18:24
Total Runtime (minutes) 74.426826
Python Version 3.12.3
Config File scripts/irm/apos_config.yml
Loading ITables v2.5.2 from the init_notebook_mode cell...
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Loading ITables v2.5.2 from the init_notebook_mode cell...
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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 levels (point-wise confidende intervals).
NoteMetadata
DoubleML Version 0.12.dev0
Script APOSCoverageSimulation
Date 2025-12-04 18:24
Total Runtime (minutes) 74.426826
Python Version 3.12.3
Config File scripts/irm/apos_config.yml
Loading ITables v2.5.2 from the init_notebook_mode cell...
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Loading ITables v2.5.2 from the init_notebook_mode cell...
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Tuning
The simulations are based on the the make_irm_data_discrete_treatments-DGP with \(500\) observations. This is only an example as the untuned version just relies on the default configuration.
APOS Coverage
The non-uniform results (coverage, ci length and bias) refer to averaged values over all levels (point-wise confidende intervals). The same holds for the loss values which are averaged over all treatment levels.
NoteMetadata
DoubleML Version 0.12.dev0
Script APOSTuningCoverageSimulation
Date 2025-12-01 13:09
Total Runtime (minutes) 38.631183
Python Version 3.12.9
Config File scripts/irm/apos_tune_config.yml
Loading ITables v2.5.2 from the init_notebook_mode cell...
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Loading ITables v2.5.2 from the init_notebook_mode cell...
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Causal Contrast Coverage
The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals). The same holds for the loss values which are averaged over all treatment levels.
NoteMetadata
DoubleML Version 0.12.dev0
Script APOSTuningCoverageSimulation
Date 2025-12-01 13:09
Total Runtime (minutes) 38.631183
Python Version 3.12.9
Config File scripts/irm/apos_tune_config.yml
Loading ITables v2.5.2 from the init_notebook_mode cell...
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Loading ITables v2.5.2 from the init_notebook_mode cell...
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