A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R

In causal inference, interference occurs when the treatment of one subject affects the outcome of other subjects. Interference can distort research conclusions about causal effects when not accounted for properly. In the absence of interference, inverse probability weighted (IPW) estimators are comm...

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Bibliographic Details
Published in:Journal of statistical software 2017, Vol.82 (2), p.1-21
Main Authors: Saul, Bradley C, Hudgens, Michael G
Format: Article
Language:English
Subjects:
causal inference
interference
SUTVA
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Summary:In causal inference, interference occurs when the treatment of one subject affects the outcome of other subjects. Interference can distort research conclusions about causal effects when not accounted for properly. In the absence of interference, inverse probability weighted (IPW) estimators are commonly used to estimate causal effects from observational data. Recently, IPW estimators have been extended to handle interference. Tchetgen Tchetgen and VanderWeele (2012) proposed IPW methods to estimate direct and indirect (or spillover) effects that allow for interference between individuals within groups. In this paper, we present , an R package that computes these IPW causal effect estimates when interference may be present within groups. We illustrate use of the package with examples from political science and infectious disease.
ISSN:1548-7660
1548-7660
DOI:10.18637/jss.v082.i02