Higher Moments Matter for Optimal Balance Weighting in Causal Estimation

alexandrossfakianakis shared this article with you from Inoreader Higher Moments Matter for Optimal Balance Weighting in Causal Estimation Via Epidemiology - Published Ahead-of-Print We expand upon a simulation study that compared three promising methods for estimating weights for assessing the average treatment effect on the treated for binary treatments: generalized boosted models, covariate-balancing propensity scores, and entropy balance. The original study showed that generalized boosted models can outperfo rm covariate-balancing propensity scores, and entropy balance when there are likely to be non-linear associations in both the treatment assignment and outcome models and when the other two models are fine-tuned to obtain balance only on first-order moments. We explore the potential benefit of using higher-order moments in the balancing conditions for covariate-balancing propensity scores and entry balance. Our findings showcase that these two models should, by default, include higher order moments and focusing only on first moments can result in substantial bias in estimated treatment effect estimates from both models that could be avoided using higher moments. We expand upon a simulation study that compared three promising methods for estimating weights for assessing the average treatment effect on the treated for binary treatments: generalized boosted models, covariate-balancing propensity scores, and entropy balance. The original study showed that generalized boosted models can outperform covariate-balancing propensity scores, and entropy balance when there are likely to be non-linear associations in both the treatment assignment and outcome models and when the other two models are fine-tuned to obtain balance only on first-order moments. We explore the potential benefit of using higher-order moments in the balancing conditions for covariate-balancing propensity scores and entry balance. Our findings showcase that these two models should, by default, include higher order moments and focusing only on first moments can result in substantial bias in estimated treatment effect estimates from both models that could be avoided using higher mom ents. Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.