Oxford Events, the new replacement for OxTalks, will launch on 16th March. From now until the launch of Oxford Events, new events cannot be published or edited on OxTalks while all existing records are migrated to the new platform. The existing OxTalks site will remain available to view during this period.
From 16th, Oxford Events will launch on a new website: events.ox.ac.uk, and event submissions will resume. You will need a Halo login to submit events. Full details are available on the Staff Gateway.
We propose a new unified framework for causal inference when the counterfactual outcomes of interest depend on how agents are linked in a network. Such network interference describes a vast literature on spillovers, social interactions, social learning, information diffusion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other agents and connections nearby as measured by path distance. Counterfactual predictions are then made by pooling outcome data across similarly configured agents. In the paper, we introduce a new nonparametric regression function for causal inference with network interference, propose a k-nearest-neighbor estimator, and provide non-asymptotic bounds on mean squared error. We demonstrate our approach by estimating the causal impact of various network interventions on social capital formation in a many-networks experiment.
Please sign up for meetings here: docs.google.com/spreadsheets/d/1GRwPBmtpUwstC4fdLZrnxfnARNYHedHykoRZG4Xq2Bo/edit#gid=0
