CATEs

CATE Coverage

The simulations are based on the the make_heterogeneous_data-DGP with \(2000\) observations. The groups are defined based on the first covariate, analogously to the CATE IRM Example, but rely on LightGBM to estimate nuisance elements (due to time constraints).

The non-uniform results (coverage, ci length and bias) refer to averaged values over all groups (point-wise confidende intervals).

Metadata

DoubleML Version                    0.10.dev0
Script                   irm_cate_coverage.py
Date                      2025-02-17 14:09:01
Total Runtime (seconds)           5386.899613
Python Version                         3.12.9
Number of observations                   2000
Number of repetitions                    1000

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

Learner g	Learner m	Bias	CI Length	Coverage	Uniform CI Length	Uniform Coverage
LGBM	LGBM	0.148	0.795	0.972	1.690	1.000
LGBM	Logistic Regression	0.059	0.281	0.942	0.599	0.996
Lasso	LGBM	0.158	0.766	0.949	1.634	1.000
Lasso	Logistic Regression	0.062	0.295	0.941	0.629	0.997

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

Learner g	Learner m	Bias	CI Length	Coverage	Uniform CI Length	Uniform Coverage
LGBM	LGBM	0.148	0.667	0.936	1.689	1.000
LGBM	Logistic Regression	0.059	0.235	0.889	0.596	0.996
Lasso	LGBM	0.158	0.643	0.896	1.632	1.000
Lasso	Logistic Regression	0.062	0.247	0.889	0.630	0.998