Causal Inference from Observational Studies with Partial Interference

• 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
• 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

Abstract

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.