OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
If staff have any questions about the Oxford Events launch, please contact halo@digital.ox.ac.uk
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
