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 0.11.dev0
Script APOCoverageSimulation
Date 2025-06-05 13:49
Total Runtime (minutes) 73.869388
Python Version 3.12.3
Config File scripts/irm/apo_config.yml
Loading ITables v2.4.2 from the init_notebook_mode cell...
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Loading ITables v2.4.2 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 quantiles (point-wise confidende intervals).
Metadata
DoubleML Version 0.11.dev0
Script APOSCoverageSimulation
Date 2025-06-05 13:49
Total Runtime (minutes) 73.850344
Python Version 3.12.3
Config File scripts/irm/apos_config.yml
Loading ITables v2.4.2 from the init_notebook_mode cell...
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Loading ITables v2.4.2 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 quantiles (point-wise confidende intervals).
Metadata
DoubleML Version 0.11.dev0
Script APOSCoverageSimulation
Date 2025-06-05 13:49
Total Runtime (minutes) 73.850344
Python Version 3.12.3
Config File scripts/irm/apos_config.yml
Loading ITables v2.4.2 from the init_notebook_mode cell...
(need help?)
Loading ITables v2.4.2 from the init_notebook_mode cell...
(need help?)