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B0578
Title: Toward valid causal and statistical inference with social network data Authors:  Elizabeth Ogburn - Johns Hopkins University (United States) [presenting]
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. When social relations can engender dependence in the variables of interest, treating such observations as independent results in invalid, anticonservative statistical inference, but there is a dearth of methods that can account for this kind of dependence. We develop a test for network dependence that can be used to screen for the appropriateness of i.i.d. statistical methods and apply it to data from the Framingham Heart Study (FHS). Our results suggest that some of the many decades worth of research on coronary heart disease and other health outcomes using FHS data could be invalid due to unacknowledged network dependence. We also extend recent work on causal inference for causally connected units to more general social network settings: we describe estimation and inference for causal effects that are specifically of interest in social network settings, and our asymptotic results allow for dependence of each observation on a growing number of other units as sample size increases.