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 2025-05-22 13:10:29
Total Runtime (seconds) 5871.561926
Python Version 3.12.10
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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 0.10.dev0
Script irm_apo_coverage.py
Date 2025-05-22 13:10:29
Total Runtime (seconds) 5871.561926
Python Version 3.12.10
Loading ITables v2.4.0 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 0.10.dev0
Script irm_apo_coverage.py
Date 2025-05-22 13:10:29
Total Runtime (seconds) 5871.561926
Python Version 3.12.10
Loading ITables v2.4.0 from the init_notebook_mode cell...
(need help?)
Loading ITables v2.4.0 from the init_notebook_mode cell...
(need help?)