Intervening on Network Ties

Eli Sherman, Ilya Shpitser
Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, PMLR 115:975-984, 2020.

Abstract

A foundational tool for making causal inferences is the emulation of randomized control trials via variable interventions. This approach has been applied to a wide variety of contexts, from health to economics [3, 6]. Variable interventions have long been studied in independent and identically distributed (iid) data contexts, but recently non-iid settings, such as networks with interacting agents [8, 17, 28] have attracted interest. In this paper, we propose a type of structural intervention [12] relevant in network contexts: the network intervention. Rather than estimating the effect of changing variables, we consider changes to social network structure resulting from creation or severance of ties between agents. We define the individual participant and average bystander effects for these interventions and describe identification criteria. We then prove a series of theoretical results that show existing identification theory obtains minimally KL-divergent distributions corresponding to network interventions. Finally, we demonstrate estimation of effects of network interventions via a simulation study.

Cite this Paper

BibTeX
@InProceedings{pmlr-v115-sherman20a, title = {Intervening on Network Ties}, author = {Sherman, Eli and Shpitser, Ilya}, booktitle = {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference}, pages = {975--984}, year = {2020}, editor = {Adams, Ryan P. and Gogate, Vibhav}, volume = {115}, series = {Proceedings of Machine Learning Research}, month = {22--25 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v115/sherman20a/sherman20a.pdf}, url = {https://proceedings.mlr.press/v115/sherman20a.html}, abstract = {A foundational tool for making causal inferences is the emulation of randomized control trials via variable interventions. This approach has been applied to a wide variety of contexts, from health to economics [3, 6]. Variable interventions have long been studied in independent and identically distributed (iid) data contexts, but recently non-iid settings, such as networks with interacting agents [8, 17, 28] have attracted interest. In this paper, we propose a type of structural intervention [12] relevant in network contexts: the network intervention. Rather than estimating the effect of changing variables, we consider changes to social network structure resulting from creation or severance of ties between agents. We define the individual participant and average bystander effects for these interventions and describe identification criteria. We then prove a series of theoretical results that show existing identification theory obtains minimally KL-divergent distributions corresponding to network interventions. Finally, we demonstrate estimation of effects of network interventions via a simulation study.} }
Endnote
%0 Conference Paper %T Intervening on Network Ties %A Eli Sherman %A Ilya Shpitser %B Proceedings of The 35th Uncertainty in Artificial Intelligence Conference %C Proceedings of Machine Learning Research %D 2020 %E Ryan P. Adams %E Vibhav Gogate %F pmlr-v115-sherman20a %I PMLR %P 975--984 %U https://proceedings.mlr.press/v115/sherman20a.html %V 115 %X A foundational tool for making causal inferences is the emulation of randomized control trials via variable interventions. This approach has been applied to a wide variety of contexts, from health to economics [3, 6]. Variable interventions have long been studied in independent and identically distributed (iid) data contexts, but recently non-iid settings, such as networks with interacting agents [8, 17, 28] have attracted interest. In this paper, we propose a type of structural intervention [12] relevant in network contexts: the network intervention. Rather than estimating the effect of changing variables, we consider changes to social network structure resulting from creation or severance of ties between agents. We define the individual participant and average bystander effects for these interventions and describe identification criteria. We then prove a series of theoretical results that show existing identification theory obtains minimally KL-divergent distributions corresponding to network interventions. Finally, we demonstrate estimation of effects of network interventions via a simulation study.
APA
Sherman, E. & Shpitser, I.. (2020). Intervening on Network Ties. Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, in Proceedings of Machine Learning Research 115:975-984 Available from https://proceedings.mlr.press/v115/sherman20a.html.

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