Date: 2020-01-31

Time: 15:30-16:30

Location: BURNSIDE 1104

Abstract:

I’ll discuss the use of observational data to estimate the causal effect of a treatment on an outcome. This task is complicated by the presence of “confounders” that influence both treatment and outcome, inducing observed associations that are not causal. Causal estimation is achieved by adjusting for this confounding by using observed covariate information. I’ll discuss the case where we observe covariates that carry sufficient information for the adjustment. But where explicit models relating treatment, outcome, covariates and confounding are not available.

Speaker

Victor Veitch is currently a Distinguished Postdoctoral Researcher in the department of statistics at Columbia University, where he works with the groups of David Blei and Peter Orbanz. He completed my Ph.D. in statistics at the University of Toronto, where he was advised by Daniel Roy. In a previous life, he worked on quantum computing at the University of Waterloo.