Hi everyone! This week at the Applied Statistics Workshop we will be welcoming *Edo Airoldi*, Professor of Statistics at Harvard University. He will be presenting joint work with Don Rubin and Daniel Sussman entitled *Estimating Causal Effects in the Presence of Interfering Units**.* Please find the abstract below and on the website <http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/1222015-edo-airoldi-harvard-title-coming-soon> . As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and lunch will be provided. See you all there! To view previous Applied Statistics presentations, please visit the website <http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>. -- Aaron Kaufman Title: Estimating Causal Effects in the Presence of Interfering Units Abstract: Classical approaches to causal inference largely rely on the assumption of “lack of interference”, according to which the outcome of an individual does not depend on the treatment assigned to others. In many applications, however, such as designing and evaluating the effectiveness of healthcare interventions that leverage social structure, assuming lack of interference is untenable. In fact, the effect of interference itself is often an inferential target of interest. In this talk, we will discuss technical issues that arise in estimating causal effects when interference can be attributed to a network among the units of analysis, and develop a strategy for optimal experimental design in this context that involves a piecewise constant approximation of a certain graphon. -- Aaron R Kaufman PhD Candidate, Harvard University Department of Government 818.263.5583