Kernel Optimal Orthogonality Weighting: A Balancing Approach to Estimating Effects of Continuous Treatments

Abstract

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.

References in corpus (6)

• Scikit-learn: Machine Learning in Python
• Policy Evaluation and Optimization with Continuous Treatments
• Matching on Generalized Propensity Scores with Continuous Exposures
• More robust estimation of sample average treatment effects using Kernel Optimal Matching in an observational study of spine surgical interventions
• Permutation Weighting
• Causal Inference for Complex Longitudinal Data: The Continuous Time g-Computation Formula

Cited by in corpus (5)

• Independence weights for causal inference with continuous treatments
• A Bayesian Gaussian Process for Estimating a Causal Exposure Response Curve in Environmental Epidemiology
• A Balancing Weight Framework for Estimating the Causal Effect of General Treatments
• Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks
• Optimal transport weights for causal inference