Short course given by Francis DiTraglia

• Department of Economics

Short course given by Francis DiTraglia from Oxford University

Testing IV validity | 16:00 - 17:15

Causal inference from observational data relies on strong assumptions. Can these assumptions be tested? What happens if they fail? This lecture takes a second look at the instrumental variables model, both the 'textbook' homogeneous effects version and the local average treatment effect (LATE) variant. Contrary to popular belief, it is sometimes possible to test the instrumental variables assumptions, even with a single instrument.

Causal inference in the presence of spillovers | 17:30 - 18:45

A fundamental assumption in traditional randomised controlled trials is the absence of spillovers: that assigning a particular treatment to one participant has no causal effect on any other participant. In practice, however, spillovers between experimental participants are common in social science, arising through diverse mechanisms such as displacement, contagion, and strategic interactions. This lecture will provide an overview of designing and analysing randomised controlled trials in the presence of spillovers.