Basic PLR Models

ATE Coverage

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

DoubleML Version                   0.10.dev0
Script                   plr_ate_coverage.py
Date                     2025-05-22 13:19:23
Total Runtime (seconds)           6440.37046
Python Version                        3.12.3

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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 \(|\rho|=1\).

DoubleML Version                      0.10.dev0
Script                   plr_ate_sensitivity.py
Date                        2025-05-22 14:16:38
Total Runtime (seconds)             9875.844212
Python Version                           3.12.3

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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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