Basic PLR Models

init_notebook_modetrusted

ATE Coverage

The simulations are based on the the make_plr_CCDDHNR2018-DGP with \(500\) observations.

Metadata

DoubleML Version                              0.11.dev0
Script                         PLRATECoverageSimulation
Date                                   2025-06-05 15:50
Total Runtime (minutes)                      194.212641
Python Version                                   3.12.3
Config File              scripts/plm/plr_ate_config.yml

Partialling out

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IV-type

For the IV-type score, the learners ml_l and ml_g are both set to the same type of learner (here Learner g).

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ATE Sensitivity

The simulations are based on the the make_confounded_plr_data-DGP with \(1000\) observations as highlighted in the Example Gallery. As the DGP is nonlinear, we will only use corresponding learners. Since the DGP includes unobserved confounders, we would expect a bias in the ATE estimates, leading to low coverage of the true parameter.

Both sensitivity parameters are set to \(cf_y=cf_d=0.1\), 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 upper 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 \(|\rho|=1\).

Metadata

DoubleML Version                                          0.11.dev0
Script                          PLRATESensitivityCoverageSimulation
Date                                               2025-06-05 16:23
Total Runtime (minutes)                                  227.226308
Python Version                                               3.12.3
Config File              scripts/plm/plr_ate_sensitivity_config.yml

Partialling out

Loading ITables v2.4.2 from the init_notebook_mode cell... (need help?)

IV-type

For the IV-type score, the learners ml_l and ml_g are both set to the same type of learner (here Learner g).

Loading ITables v2.4.2 from the init_notebook_mode cell... (need help?)