SSM under Nonignorable Nonresponse

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ATE Coverage

The simulations are based on the the make_ssm_data-DGP with \(500\) observations. The simulation considers data with nonignorable nonresponse.

DoubleML Version                                    0.8.2
Script                   ssm_nonignorable_ate_coverage.py
Date                                  2024-08-11 18:21:13
Total Runtime (seconds)                       3193.123976
Python Version                                     3.12.4
Coverage for 95.0%-Confidence Interval over 1000 Repetitions
Learner g Learner m Learner pi Bias CI Length Coverage
LGBM LGBM LGBM 3.667 15.499 0.966
LGBM LGBM Logistic 1.327 5.758 0.974
LGBM Logistic LGBM 1.232 5.472 0.959
LGBM Logistic Logistic 0.728 3.015 0.940
Lasso LGBM LGBM 2.892 12.159 0.975
Lasso LGBM Logistic 2.051 8.416 0.974
Lasso Logistic LGBM 1.187 4.970 0.950
Lasso Logistic Logistic 0.566 2.375 0.928
Coverage for 90.0%-Confidence Interval over 1000 Repetitions
Learner g Learner m Learner pi Bias CI Length Coverage
LGBM LGBM LGBM 3.667 13.007 0.919
LGBM LGBM Logistic 1.327 4.832 0.918
LGBM Logistic LGBM 1.232 4.593 0.897
LGBM Logistic Logistic 0.728 2.530 0.867
Lasso LGBM LGBM 2.892 10.204 0.931
Lasso LGBM Logistic 2.051 7.063 0.917
Lasso Logistic LGBM 1.187 4.171 0.894
Lasso Logistic Logistic 0.566 1.993 0.870