Draft

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                     0.10.dev0
Script                   pliv_late_coverage.py
Date                       2025-01-08 17:34:57
Total Runtime (seconds)           19185.318093
Python Version                          3.12.8

Partialling out

Table 1: Coverage for 95.0%-Confidence Interval over 1000 Repetitions
Table 2: Coverage for 90.0%-Confidence Interval over 1000 Repetitions

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).

Table 3: Coverage for 95.0%-Confidence Interval over 1000 Repetitions
Table 4: Coverage for 90.0%-Confidence Interval over 1000 Repetitions