Online Program Home
My Program

Abstract Details

Activity Number: 563 - Mechanisms of Interference: New Strategies for Identification and Estimation
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Social Statistics Section
Abstract #326952
Title: Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks
Author(s): Laura Forastiere* and Edoardo M Airoldi
Companies: Yale and Harvard University

Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of local interference, for instance, potential outcomes of a unit depend on its treatment as well as on the treatments of other local units, such as its neighbors according to the network. In observational studies, a further complication is that the typical unconfoundedness assumption must be extended---say, to include the treatment of neighbors, and individual and neighborhood covariates---to guarantee identification and valid inference. Here, we propose new estimands that define treatment and interference effects. We then derive analytical expressions for the bias of a naive estimator that wrongly assumes away interference. The bias depends on the level of interference but also on the degree of association between individual and neighborhood treatments. We propose an extended unconfoundedness assumption that accounts for interference, and we develop new covariate-adjustment methods that lead to valid estimates of treatment and interference effects in observational studies on networks. Estimation is based on a general

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

Back to the full JSM 2018 program