A nonparametric doubly robust test for a continuous treatment effect

Understand

The vast majority of literature on evaluating the significance of a treatment effect based on observational data has been confined to discrete treatments.

• These methods are not applicable to drawing inference for a continuous treatment, which arises in many important applications.
• To adjust for confounders when evaluating a continuous treatment, existing inference methods often rely on discretizing the treatment or using (possibly misspecified) parametric models for the effect curve.
• Recently, Kennedy et al.