PLIV Models

init_notebook_modetrusted

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.

Metadata

DoubleML Version                     0.10.dev0
Script                   pliv_late_coverage.py
Date                       2025-05-22 16:55:09
Total Runtime (seconds)           19353.436544
Python Version                         3.12.10

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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