A central problem in the analysis of observational data is inferring casual relationships - what are the underlying causes of the observed behaviours? With the recent proliferation of Big Data from online social networks, it has become important to determine to what extent social influence causes certain messages to 'go viral', and to what extent other causes also play a role. In this thesis, we propose a methodological framework for quantitatively measuring and for qualifying the effects of social influence from Web-mediated interactions, while accounting for other relevant causes, on individual and collective outcomes, using 'found' observational digital data. This framework is based on causality theory and is informed by the social sciences, constituting a methodological contribution of the type that is much needed in the emergent interdisciplinary area of computational social science. We demonstrate theoretically and empirically how our framework offers a way for successfully addressing many of the limitations of the popular information diffusion-based paradigm for social influence online, enabling researchers to disentangle, measure and qualify the effects of social influence from online interactions, at the individual and the collective level.
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