PLIV Models

LATE Coverage

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.

DoubleML Version                                0.11.dev0
Script                         PLIVLATECoverageSimulation
Date                                     2025-09-08 12:11
Total Runtime (minutes)                        332.748483
Python Version                                     3.12.3
Config File              scripts/plm/pliv_late_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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