"Inference in the Presence of Network Dependence Due to Contagion" (Next Week at the Statistics Seminar)

Attention conservation notice: Only of interest if you (1) care about statistical inference with network data, and (2) will be in Pittsburgh next week.

A (perhaps) too-skeptical view of statistics is that we should always think we have $ n=1 $, because our data set is a single, effectively irreproducible, object. With a lot of care and trouble, we can obtain things very close to independent samples in surveys and experiments. When we get to time series or spatial data, independence becomes a myth we must abandon, but we still hope that we can break up the data set into many nearly-independent chunks. To make those ideas plausible, though, we need to have observations which are widely separated from each other. And those asymptotic-independence stories themselves seem like myths when we come to networks, where, famously, everyone is close to everyone else. The skeptic would, at this point, refrain from drawing any inference whatsoever from network data. Fortunately for the discipline, Betsy Ogburn is not such a skeptic.

Elizabeth Ogburn, "Inference in the Presence of Network Dependence Due to Contagion"

Abstract: Interest in and availability of social network data has led to increasing attempts to make causal and statistical inferences using data collected from subjects linked by social network ties. But inference about all kinds of estimands, starting with simple sample means, is challenging when only a single network of non-independent observations is available. There is a dearth of principled methods for dealing with the dependence that such observations can manifest. We describe methods for causal and semiparametric inference when the dependence is due solely to the transmission of information or outcomes along network ties.

Time and place: 4--5 pm on Monday, 16 November 2015, in 1112 Doherty Hall

As always, the talk is free and open to the public.

Enigmas of Chance; Networks