On 28th November OxTalks will move to the new Halo platform and will become 'Oxford Events' (full details are available on the Staff Gateway).
There will be an OxTalks freeze beginning on Friday 14th November. This means you will need to publish any of your known events to OxTalks by then as there will be no facility to publish or edit events in that fortnight. During the freeze, all events will be migrated to the new Oxford Events site. It will still be possible to view events on OxTalks during this time.
If you have any questions, 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.
