Balancing Covariates for Continuous Treatments by Exploiting Continuity

Abstract

We consider causal effect estimation in observational studies with continuous treatment. We introduce a general class of causal estimands that, as a special case, includes the average causal effect of assigning treatment according to an arbitrary counterfactual distribution. Balancing estimators are particularly attractive tools for covariate adjustment in this setting because they avoid directly modeling the treatment assignment mechanism, thereby reducing the potential for instability and misspecification bias in subsequent causal effect estimation. In this paper, we develop a general framework of covariate balancing for continuous treatment with the proposed class of causal estimands, leveraging the assumption of continuity of treatment dose. As in the binary treatment setting, analysts specify the basis functions to be balanced, the allowable degree of covariate imbalance, and a criterion for selecting among admissible weights. Under a minimum variance criterion for choosing weights, we establish consistency, characterize the effect of basis misspecification, and prove asymptotic normality. We develop consistent variance estimation, which yields asymptotically valid confidence intervals. Under homoskedasticity, our estimator is provably more efficient than inverse probability weighting based on the true propensity score. Simulation studies support our theoretical results, and we illustrate the proposed method by estimating the effect of vocational training hours on expected future earnings using data from the U.S. Job Corps study.