The simulations are based on the the make_pliv_CHS2015-DGP with \(500\) observations. Due to the linearity of the DGP, Lasso is a nearly optimal choice for the nuisance estimation.
Metadata
DoubleML Version 0.11.dev0
Script PLIVLATECoverageSimulation
Date 2025-06-05 18:09
Total Runtime (minutes) 333.494711
Python Version 3.12.3
Config File scripts/plm/pliv_late_config.yml
Partialling out
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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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...
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Loading ITables v2.4.2 from the init_notebook_mode cell...
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