Higher Moments for Optimal Balance Weighting in Causal Estimation. Issue 4 (12th April 2022)

• Record Type: Journal Article
• Authors: Huang, Melody Y.; Vegetabile, Brian G.; Burgette, Lane F.; Setodji, Claude; Griffin, Beth Ann
• Is Part Of: Epidemiology. Volume 33:Issue 4(2022)
• Page Start: 551
• Page End: 554
• Publication Date: 2022-04-12
• Subjects: Causal inference -- Observational study -- Propensity score -- Entropy balance -- Higher-order moments -- Balance; Epidemiology -- Periodicals; Epidemiology -- Environmental aspects -- Periodicals; Epidemiology -- Periodicals; 614.405
• DOI: 10.1097/EDE.0000000000001481
• Languages: English
• ISSNs: 1044-3983

Abstract

Abstract : 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 nonlinear 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 entropy 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.