Applied Statistics - Guido Imbens 

 This week, the Applied Statistics Workshop will present a talk by   Guido Imbens   of the University of California at Berkeley Department of Economics.  Professor Imbens is currently a visiting professor in the Harvard Economics Department and is one of the faculty sponsors of the Applied Statistics Workshop, so we are delighted that he will be speaking to the group.  He received his Ph.D. from Brown University and has served on the faculties of Harvard and UCLA before moving to Berkeley.  He has published widely, with a particular focus on questions relating to causal inference.     

 Professor Imbens will present a talk entitled "  Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand . "  If you have been following the discussion on achieving balance taking place on the blog, then this talk should be of great interest.  It considers situations in which balance is difficult to achieve in practice, and suggests that estimating treatment effects for statistically defined subsamples may produce better results.  The presentation will be at noon on Wednesday, November 2 in Room N354, CGIS North, 1737 Cambridge St. Lunch will be provided.  The abstract of the paper follows on the jump: 
 


 
Estimation of average treatment effects under unconfoundedness or selection on observables is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used informal methods for trimming the sample or focused on subpopulations of interest. In this paper we develop formal methods for addressing such lack of overlap in which we sacrifice some external validity in exchange for improved internal validity. We characterize optimal subsamples where the average treatment effect can be estimated most precisely, as well optimally weighted average treatment effects. We show the problem of lack of overlap has important connections to the presence of treatment effect heterogeneity: under the assumption of constant conditional average treatment effects the treatment effect can be estimated much more precisely. The efficient estimator for the treatment effect under the assumption of a constant conditional average treatment effect is shown to be identical to the efficient estimator for the optimally weighted average treatment effect. We also develop tests for the null hypotheses of a constant and a zero conditional average treatment effect. The latter is in practice more powerful than the commonly used test for a zero average treatment effect.