Optimal design of experiments on connected units with application to social networks
Optimal design of experiments on connected units with application to social networks
When experiments are performed on social networks, it is difficult to justify the usual assumption of treatment-unit additivity, due to the connections between actors in the network. We investigate how connections between experimental units affect the design of experiments on those experimental units. Specifically, where we have unstructured treatments, whose effects propagate according to a linear network effects model which we introduce, we show that optimal designs are no longer necessarily balanced; we further demonstrate how experiments which do not take a network effect into account can lead to much higher variance than necessary and/or a large bias. We show the use of this methodology in a very wide range of experiments in agricultural trials, and crossover trials, as well as experiments on connected individuals in a social network.
design of experiments, social networks, optimal design, Linear modelling, Network science, Linear network effects mode, Experimental design
1-24
Parker, Ben
26c5a5ab-17b3-4d6c-ae11-abf3a2554529
Gilmour, Steven
984dbefa-893b-444d-9aa2-5953cd1c8b03
Schormans, John
17a57e50-4342-4278-8b7a-27bfecd6837b
28 February 2017
Parker, Ben
26c5a5ab-17b3-4d6c-ae11-abf3a2554529
Gilmour, Steven
984dbefa-893b-444d-9aa2-5953cd1c8b03
Schormans, John
17a57e50-4342-4278-8b7a-27bfecd6837b
Parker, Ben, Gilmour, Steven and Schormans, John
(2017)
Optimal design of experiments on connected units with application to social networks.
Journal of the Royal Statistical Society: Series C (Applied Statistics), .
(doi:10.1111/rssc.12170).
Abstract
When experiments are performed on social networks, it is difficult to justify the usual assumption of treatment-unit additivity, due to the connections between actors in the network. We investigate how connections between experimental units affect the design of experiments on those experimental units. Specifically, where we have unstructured treatments, whose effects propagate according to a linear network effects model which we introduce, we show that optimal designs are no longer necessarily balanced; we further demonstrate how experiments which do not take a network effect into account can lead to much higher variance than necessary and/or a large bias. We show the use of this methodology in a very wide range of experiments in agricultural trials, and crossover trials, as well as experiments on connected individuals in a social network.
Text
socialNetworksJRSSCv4-EditorsResponse.pdf
- Accepted Manuscript
More information
Accepted/In Press date: 30 June 2016
e-pub ahead of print date: 13 August 2016
Published date: 28 February 2017
Keywords:
design of experiments, social networks, optimal design, Linear modelling, Network science, Linear network effects mode, Experimental design
Organisations:
Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 383913
URI: http://eprints.soton.ac.uk/id/eprint/383913
ISSN: 0035-9254
PURE UUID: 1e28c970-8c4a-43e4-b16d-6f47afbe84c0
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Date deposited: 18 Nov 2015 14:06
Last modified: 14 Mar 2024 21:49
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Contributors
Author:
Ben Parker
Author:
Steven Gilmour
Author:
John Schormans
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