19: Doubly Robust Pivotal Confidence Intervals for a Monotonic Continuous Treatment Effect Curve

Music City Center 

A large majority of literature on evaluating the significance of a treatment effect based on observational data has been focused on discrete treatments. These methods are not applicable to drawing inference for a continuous treatment, which arises in many important applications. Here, we develop doubly robust confidence intervals for the continuous treatment effect curve (at a fixed point) under the assumption that it is monotonic, by developing a likelihood ratio-type procedure. Monotonicity is often a very natural assumption in the setting of a continuous treatment effect curve, and the assumption of monotonicity removes the need to choose a smoothing parameter for the nonparametrically estimated curve (or the related need to estimate the curve's unknown bias which is challenging). We illustrate the new methods via simulations and a study of a dataset relating the effect of nurse staffing hours on hospital performance.