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.
Beliefs and decisions are often based on confronting models with data. What is the largest “fake” correlation that a misspecified model can generate, even when it passes an elementary misspecification test? We study an “analyst” who fits a model, represented by a directed acyclic graph, to an objective (multivariate) Gaussian distribution. We characterize the maximal estimated pairwise correlation for generic Gaussian objective distributions, subject to the constraint that the estimated model preserves the marginal distribution of any individual variable. As the number of model variables grows, the estimated correlation can become arbitrarily close to one, regardless of the objective correlation
Link to paper: 297ff237-12bb-4686-9983-60da8eb1eb5d.filesusr.com/ugd/90366b_f660300e9e8a4b1fabd5e35c6c8a78f4.pdf
Please sign up for meetings here: docs.google.com/spreadsheets/d/1G0KdCfEkG4LYBuDSCLxyGRSEULv3_smLEEQMofG4X5U/edit#gid=0
