MIT Stochastics & Statistics Seminar Series: Edo Airoldi
Abstract: Classical approaches to causal inference largely rely on the assumption of “lack of interference”, according to which the outcome of each individual does not depend on the treatment assigned to others. In many applications, however, including healthcare interventions in schools, online education, and design of online auctions and political campaigns on social media, assuming lack of interference is untenable. In this talk, Prof. Airoldi will introduce some fundamental ideas to deal with interference in causal analyses, focusing on situations where interference can be attributed to a network among the units of analysis, and offer new results and insights for estimating causal effects in this context.
Bio: Edoardo Airoldi is an associate professor of statistics at Harvard University, on sabbatical leave, visiting CSAIL and the new Institute for Data, Systems and Society at MIT. His research interests include statistical methodology and theory for network data analysis, stochastic optimization for inference with large data sets, and applications to complex social and biological systems. He is the recipient of several honors, including an ONR Young Investigator Award, an NSF CAREER Award, a Sloan Research Fellowship, and a Shutzer Fellowship, has received more than ten outstanding paper awards, including the W. J. Youden Award and the John Van Ryzin Award for his research in statistics and biology, the Thomas R. Ten Have Award for his research in causal inference, the LinkedIn Economic Graph Challenge Award for his work on computational social science and economics, and was recently a speaker at the Sackler Colloquium on “Drawing Causal Inference from Big Data” at the National Academy of Sciences.
For complete series listing please click here.