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Activity Number: 14
Type: Topic Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #318559
Title: Causal Inference from Observational Studies with Partial Interference
Author(s): Brian Barkley* and Michael Hudgens
Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Keywords: Causal inference ; Counterfactual propensity score ; Estimand ; Infectious disease ; Interference ; Observational study

Inferring causal effects from an observational study is challenging because participants are not randomized to treatment. Observational studies in infectious disease research include the additional challenge that one participant's treatment may affect another participant's outcome, i.e., there may be interference. In this paper we will discuss recent approaches to defining causal effects in the presence of interference and propose a new class of causal estimands based on counterfactual propensity scores. Inverse probability-weighted estimators for these estimands are considered. The large sample properties of the estimators are derived, a simulation study showing the finite sample performance of the estimators is presented, and the proposed methods are illustrated by analyzing data from a study of cholera vaccination in over 100,000 individuals in Matlab, Bangladesh.

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

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