Dear all, 

We would like to draw your attention to this organized session on causal inference in social network analysis. The call for abstracts is open until 31.01.2020. We look forward to seeing you in Paris!

Kind regards, 
Sebastián Martínez


A very important aspect of social network research is finding ways to improve individuals' lives by acknowledging their social context. However, reaching causal conclusions in network studies where there was no treatment or where it could not be randomised is limited at best, since most traditional causal inference methods require assuming observations to be independent from each other. This assumption is clearly not valid in the network context, as individuals are by definition interconnected. In exchange for other assumptions, the causal inference literature has made some progress in the last decade by using the potential outcomes framework, permutation inference, and inverse probability weighted estimators to account for the presence of interference. Experimental studies have also pushed the boundaries of this research agenda by using interventions where treatment gets randomised - either to isolated networks, or specific individuals. We invite participants who want to contribute their work, progress, questions or comments on causal inference in social network analysis. The session is intended for researchers in every field interested in the intersection of these topics - we hope for theoretical, practical, empirical, and even epistemological contributions.

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