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Activity Number: 639 - Causal Inference Meets Statistical Learning with Complex Data
Type: Invited
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #326645 Presentation
Title: Discovering Effect Modification in Observational Studies
Author(s): Dylan Small* and Jesse Yenchih Hsu and Paul Rosenbaum and Kwonsang Lee and Jose Zubizarreta and Jeffrey Silber
Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and Harvard University and Harvard University and University of Pennsylvania
Keywords: causal inference; sensitivity analysis; treatment effect heterogeneity

There is effect modification if the magnitude or stability of a treatment effect varies systematically with the level of an observed covariate. A larger or more stable treatment effect is typically less sensitive to bias from unmeasured covariates, so it is important to recognize effect modification when it is present. Additionally, effect modification is of interest for personalizing treatments, for example using genetic information to choose the best treatment for a patient. We present a method for conducting a sensitivity analysis in an observational study that empirically discovers effect modification by exploratory methods, but controls the family-wise error rate or false discovery rate in discovered groups.

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

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