Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R. | Saul | Journal of Statistical Software
Authors: Bradley C. Saul, Michael G. Hudgens
Title: A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R.
Abstract: 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 inferference, 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.

Page views:: 1947. Submitted: 2015-10-19. Published: 2017-11-29.
Paper: A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R.     Download PDF (Downloads: 710)
Supplements:
inferference_1.0.0.tar.gz: R source package Download (Downloads: 45; 288KB)
v82i02.R: R replication code Download (Downloads: 65; 4KB)

DOI: 10.18637/jss.v082.i02

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.