The simulations are based on the the make_irm_data-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 IRMATECoverageSimulation
Date 2025-06-05 14:42
Total Runtime (minutes) 125.660617
Python Version 3.12.3
Config File scripts/irm/irm_ate_config.yml
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ATTE Coverage
As for the ATE, the simulations are based on the the make_irm_data-DGP with \(500\) observations.
Metadata
DoubleML Version 0.11.dev0
Script IRMATTECoverageSimulation
Date 2025-06-05 14:42
Total Runtime (minutes) 126.0816
Python Version 3.12.3
Config File scripts/irm/irm_atte_config.yml
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Sensitivity
The simulations are based on the the make_confounded_irm_data-DGP with \(5,000\) observations. Since the DGP includes an unobserved confounder, we would expect a bias in the ATE estimates, leading to low coverage of the true parameter.
The confounding is set such that both sensitivity parameters are approximately \(cf_y=cf_d=0.1\), such that the robustness value \(RV\) should be approximately \(10\%\). Further, the corresponding confidence intervals are one-sided (since the direction of the bias is unkown), such that only one side should approximate the corresponding coverage level (here only the lower coverage is relevant since the bias is positive). Remark that for the coverage level the value of \(\rho\) has to be correctly specified, such that the coverage level will be generally (significantly) larger than the nominal level under the conservative choice of \(|\rho|=1\).
ATE
Metadata
DoubleML Version 0.11.dev0
Script IRMATESensitivityCoverageSimulation
Date 2025-06-05 13:14
Total Runtime (minutes) 37.417967
Python Version 3.12.3
Config File scripts/irm/irm_ate_sensitivity_config.yml
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ATTE
Metadata
DoubleML Version 0.11.dev0
Script IRMATTESensitivityCoverageSimulation
Date 2025-06-05 13:14
Total Runtime (minutes) 37.619707
Python Version 3.12.3
Config File scripts/irm/irm_atte_sensitivity_config.yml
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