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Activity Number: 545 - Machine Learning and Nonparametric Methods in Causal Inference
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #309763
Title: Nonparametric Tests of the Causal Null with Non-Discrete Exposures
Author(s): Ted Westling*
Companies: University of Massachusetts Amherst
Keywords: Nonparametric; causal inference; efficiency theory; dose-response; targeted learning; maching learning

In many scientific studies, it is of interest to determine whether an exposure has a causal effect on an outcome. In observational studies, this is a challenging task due to the presence of confounding variables that affect both the exposure and the outcome. Many methods have been developed to test for the presence of a causal effect when all such confounding variables are observed and when the exposure of interest is discrete. In this article, we propose a class of nonparametric tests of the null hypothesis that there is no average causal effect of an arbitrary univariate exposure on an outcome in the presence of observed confounding. Our tests apply to discrete, continuous, and mixed discrete-continuous exposures. We demonstrate that our proposed tests are doubly-robust consistent, that they have correct asymptotic type I error if both nuisance parameters involved in the problem are estimated at fast enough rates, and that they have power to detect local alternatives approaching the null at the root-n rate. We study the performance of our tests in numerical studies, and use them to test for the presence of a causal effect of smoking on birthweight among smoking mothers.

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

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