Date: 2019-02-15

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Causal inference is a challenging problem because causation cannot be established from observational data alone. Researchers typically rely on additional sources of information to infer causation from association. Such information may come from powerful designs such as randomization, or background knowledge such as information on all confounders. However, perfect designs or background knowledge required for establishing causality may not always be available in practice. In this talk, I use novel causal identification results to show that the instrumental variable approach can be used to combine the power of design and background knowledge to draw causal conclusions. I also introduce novel estimation tools to construct estimators that are robust, efficient and enjoy good finite sample properties. These methods will be discussed in the context of a randomized encouragement design for a flu vaccine.

Speaker

Linbo Wang is an Assistant Professor in the Department of Statistical Sciences, University of Toronto and Department of Computer and Mathematical Sciences, University of Toronto Scarborough.

Prior to this, he was a postdoc in the Harvard Causal Inference Program working with Eric Tchetgen Tchetgen and James Robins. He obtained a Ph.D. in Biostatistics from University of Washington in Mar 2016.