Estimation of causal effects of multiple treatments in observational studies with a binary outcome

Keyphrases

• Binary Outcome 100%
• Multiple Treatments 100%
• Causal Effect 100%
• Bayesian Additive Regression Trees 100%
• Targeted Maximum Likelihood Estimation 66%
• Common Support 33%
• Inverse Probability of Treatment Weighting 33%
• Generalized Boosted Model 33%
• Treatment Outcome 16%
• Non-small Cell Lung Cancer (NSCLC) 16%
• Continuous Outcomes 16%
• Nonlinearity 16%
• Convergence Property 16%
• Generating Mechanism 16%
• Score-based 16%
• Bias Reduction 16%
• Root Mean Square Error 16%
• Surgical Approach 16%
• Small Roots 16%
• Non-additivity 16%
• Low Bias 16%
• Treatment Assignment 16%
• Generalized Propensity Score 16%
• Regression Adjustment 16%
• Support Region 16%
• Confidence Interval Coverage 16%
• Matching Adjustment 16%
• Vector Matching 16%
• Vector Regression 16%

Mathematics

• Bayesian 100%
• Causal Effect 100%
• Regression tree 100%
• Maximum Likelihood Estimator 66%
• Covariate 33%
• Probability Theory 33%
• Common Support 33%
• Confidence Interval 16%
• Convergence Property 16%
• Squared Error 16%
• Treatment Assignment 16%
• Bias Reduction 16%
• Unique Set 16%
• Nonlinearity 16%
• Robust Method 16%