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Activity Number: 65 - New Methods for Identifying and Testing Heterogeneous Treatment Effects in One or a Pair of Studies
Type: Topic Contributed
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Health Policy Statistics Section
Abstract #301734 Presentation
Title: Inference for the Smoothed Proportion Whose Average Treatment Effect Exceeds a Threshold
Author(s): Jonathan Levy*
Keywords: heterogeneity; blip function; causal inference; kernel smoothing; targeted learning ; TMLE

The strata-specific treatment effect or so-called blip for a randomly drawn strata of confounders defines a random variable and a corresponding cumulative distribution function. However, the CDF is not pathwise differentiable, necessitating a kernel smoothing approach to estimate it at a given point or perhaps many points. Assuming the CDF is continuous, we derive the efficient influence curve of the kernel smoothed version of the blip CDF and a CV-TMLE estimator. The estimator is asymptotically efficient under two conditions, one of which involves a second order remainder term which, in this case, shows us that knowledge of the treatment mechanism does not guarantee a consistent estimate. The remainder term also teaches us exactly how well we need to estimate the nuisance parameters to guarantee asymptotic efficiency. This estimator opens up the possibility of developing methodology for optimal choice of the kernel and bandwidth to form confidence bounds for the CDF itself, which is of interest in public health or economic interventions to account for heterogeneity or to check whether large portions of the population have beneficial or deleterious effects from treatment

Authors who are presenting talks have a * after their name.

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