Kernel Optimal Orthogonality Weighting: A Balancing Approach to Estimating Effects of Continuous Treatments
This addresses model sensitivity in continuous treatment effect estimation for fields like epidemiology, though it appears incremental as an optimization-based extension of existing weighting methods.
The paper tackles the problem of estimating effects of continuous treatments, which is sensitive to model misspecification and extreme weights, by proposing Kernel Optimal Orthogonality Weighting (KOOW) to provide optimal covariate balance and control extreme weights, with evaluation in a simulation study and application to red meat consumption's effect on blood pressure.
Many scientific questions require estimating the effects of continuous treatments. Outcome modeling and weighted regression based on the generalized propensity score are the most commonly used methods to evaluate continuous effects. However, these techniques may be sensitive to model misspecification, extreme weights or both. In this paper, we propose Kernel Optimal Orthogonality Weighting (KOOW), a convex optimization-based method, for estimating the effects of continuous treatments. KOOW finds weights that minimize the worst-case penalized functional covariance between the continuous treatment and the confounders. By minimizing this quantity, KOOW successfully provides weights that orthogonalize confounders and the continuous treatment, thus providing optimal covariate balance, while controlling for extreme weights. We valuate its comparative performance in a simulation study. Using data from the Women's Health Initiative observational study, we apply KOOW to evaluate the effect of red meat consumption on blood pressure.