Statistical Science

Causal Diagrams for Interference

Elizabeth L. Ogburn and Tyler J. VanderWeele

Full-text: Open access

Abstract

The term “interference” has been used to describe any setting in which one subject’s exposure may affect another subject’s outcome. We use causal diagrams to distinguish among three causal mechanisms that give rise to interference. The first causal mechanism by which interference can operate is a direct causal effect of one individual’s treatment on another individual’s outcome; we call this direct interference. Interference by contagion is present when one individual’s outcome may affect the outcomes of other individuals with whom he comes into contact. Then giving treatment to the first individual could have an indirect effect on others through the treated individual’s outcome. The third pathway by which interference may operate is allocational interference. Treatment in this case allocates individuals to groups; through interactions within a group, individuals may affect one another’s outcomes in any number of ways. In many settings, more than one type of interference will be present simultaneously. The causal effects of interest differ according to which types of interference are present, as do the conditions under which causal effects are identifiable. Using causal diagrams for interference, we describe these differences, give criteria for the identification of important causal effects, and discuss applications to infectious diseases.

Article information

Source
Statist. Sci., Volume 29, Number 4 (2014), 559-578.

Dates
First available in Project Euclid: 15 January 2015

Permanent link to this document
https://projecteuclid.org/euclid.ss/1421330547

Digital Object Identifier
doi:10.1214/14-STS501

Mathematical Reviews number (MathSciNet)
MR3300359

Zentralblatt MATH identifier
1331.62200

Keywords
Causal diagrams causal inference contagion DAGs graphical models infectiousness interference nonparametric identification social networks spillover effects

Citation

Ogburn, Elizabeth L.; VanderWeele, Tyler J. Causal Diagrams for Interference. Statist. Sci. 29 (2014), no. 4, 559--578. doi:10.1214/14-STS501. https://projecteuclid.org/euclid.ss/1421330547


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