Higher Moments for Optimal Balance Weighting in Causal Estimation

Published in: Epidemiology, Volume 33, Issue 4, pages 551–554 (July 2022). doi: 10.1097/EDE.0000000000001481

Posted on RAND.org on June 24, 2022

by Melody Huang, Brian G. Vegetabile, Lane F. Burgette, Claude Messan Setodji, Beth Ann Griffin

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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.

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