This week at the Applied Statistics workshop we will be welcoming James Robins, the Mitchell L. and Robin LaFoley Dong Professor of Epidemiology at the Harvard Chan School of Public Health.  He will be presenting work entitled "Variable Selection for Estimation of Causal Effects - Art or Science: What is Done in Practice, What Ought to be Done, and What Cannot be Done?"  Please find the abstract below and on the website.

We will meet in CGIS Knafel Room 354 at noon and lunch will be provided.

Best,
Pam


Title: Variable Selection for Estimation of Causal Effects - Art or Science: What is Done in Practice, What Ought to be Done, and What Cannot be Done?
 
Abstract: I consider the problem of estimation of the average treatment effect (ATE) of a binary treatment in the presence of a possibly high dimensional vector of potential pre-treatment confounding factors. How does one choose which confounders to adjust for and once chosen how should one  adjust? Many  methods have been proposed and the number of such methods seem to be proliferating at a rapid rate. Authors often advocate for their proposed method based on simulation studies, often comparing their method to others using either mean square error and/or confidence interval length and coverage as a criterion. However any simulation study can explore only a small part of the “parameter space” leading to conflicting claims and recommendations. In this talk I try to begin to provide some small semblance of order to this disorder by reviewing both known and new results concerning (i) the statistical guarantees offered by each approach (ii) mathematical relationships between different approaches, and (iii) more generally the limits to the guarantees that any possible approach can offer.