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
Estimates of treatment effects using observational data can be biased due to confounding, model misspecification, and other reasons. A placebo test offers a complementary diagnostic for evaluating these threats to inference by checking for a relationship that should be found in the data if the main estimates were biased, but should be absent otherwise. Drawing on a comprehensive survey of recent empirical work in political science, this paper defines placebo tests, introduces a typology of tests, and analyzes what makes them informative (both in ideal and non-ideal circumstances). We discuss examples of placebo tests that effectively address different types of bias; we also point out tests and types of tests that we argue are largely uninformative, and we highlight the problem of null hacking in the design of placebo tests.
