Analytical and Bootstrap Confidence Intervals for Double Machine Learning: Simulation Studies and an Application to Rural-Urban Differences in Obesity Prevalence

arXiv:2607.29456 · stat.ML, cs.LG, stat.AP · Submitted 2026-07-31 · Read on arXiv

stat.ML, cs.LG, stat.AP

Submitted: 2026-07-31

Updated: 2026-08-25

Comments: 30 pages, 4 figures, 12 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Lasso — 26× in this paper · explained in 2 episodes
• Double Machine Learning — 13× in this paper
• Overfitting — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 6 episodes
• Empirical Coverage — 3× in this paper · explained in 2 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• Partially Linear Regression — 3× in this paper
• Score Function — 3× in this paper · explained in 4 episodes
• American Community Survey — 2× in this paper
• Causal Inference — 2× in this paper · explained in 5 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 9 episodes
• Centers for Disease Control and Prevention — 1× in this paper
• Composition — 1× in this paper · explained in 2 episodes
• Data Generating Processes — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Weight Decay — 1× in this paper · explained in 2 episodes

Sources

• Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration
• Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
• Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions

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