Improving propensity score weighting using machine learning

Mathematics

• Classification and Regression Trees 100%
• Propensity Score 83%
• Machine Learning 67%
• Weighting 61%
• Ensemble Methods 43%
• Logistic Regression 28%
• Covariates 22%
• Coverage 17%
• Performance 15%
• Confidence interval 14%
• Performance Metrics 13%
• Nonlinearity 12%
• Random Forest 12%
• Bias Reduction 11%
• Main Effect 10%
• Standard error 9%
• Scenarios 7%
• Sample Size 6%
• Binary 6%
• Alternatives 5%
• Model 2%

Medicine & Life Sciences

• Machine Learning 69%
• Propensity Score 65%
• Logistic Models 17%
• Confidence Intervals 10%
• Forests 10%
• Sample Size 7%
• Weights and Measures 5%