New research questions Double Machine Learning confidence interval reliability

A new research paper explores the reliability of confidence intervals in Double Machine Learning (DML) when using various machine learning algorithms for nuisance parameter estimation. The study conducted simulations comparing analytical and bootstrap confidence intervals across algorithms like LASSO, Random Forest, LightGBM, and Neural Networks. Results indicated significant variability in coverage performance based on the chosen learner, with coverage probabilities sometimes decreasing as sample size increased. The research also applied these methods to a real-world dataset examining rural-urban differences in obesity prevalence in the U.S., confirming learner choice impacts model performance and finding a statistically significant link between greater rurality and increased obesity. AI

IMPACT Highlights potential issues with inference reliability when applying flexible machine learning methods in causal analysis.

RANK_REASON Academic paper detailing a new methodology and simulation study. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• Double Machine Learning
• lasso
• LightGBM
• Neural Networks
• random forest
• U.S.

• paper
• other

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