SSM with Missingness at Random

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ATE Coverage

The simulations are based on the the make_ssm_data-DGP with \(500\) observations. The simulation considers data under missingness at random.

Metadata

DoubleML Version                           0.9.0
Script                   ssm_mar_ate_coverage.py
Date                         2024-09-09 09:44:28
Total Runtime (seconds)              8451.345644
Python Version                            3.12.5

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

Learner g	Learner m	Learner pi	Bias	CI Length	Coverage
LGBM	LGBM	LGBM	1.525	7.024	0.981
LGBM	LGBM	Logistic	0.615	3.076	0.973
LGBM	Logistic	LGBM	0.654	3.069	0.985
LGBM	Logistic	Logistic	0.127	0.643	0.958
Lasso	LGBM	LGBM	1.270	5.995	0.981
Lasso	LGBM	Logistic	0.622	2.790	0.955
Lasso	Logistic	LGBM	0.613	2.740	0.970
Lasso	Logistic	Logistic	0.123	0.610	0.961

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

Learner g	Learner m	Learner pi	Bias	CI Length	Coverage
LGBM	LGBM	LGBM	1.525	5.895	0.934
LGBM	LGBM	Logistic	0.615	2.582	0.927
LGBM	Logistic	LGBM	0.654	2.576	0.945
LGBM	Logistic	Logistic	0.127	0.540	0.914
Lasso	LGBM	LGBM	1.270	5.031	0.939
Lasso	LGBM	Logistic	0.622	2.341	0.887
Lasso	Logistic	LGBM	0.613	2.300	0.919
Lasso	Logistic	Logistic	0.123	0.512	0.897