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Activity Number: 296
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #318651
Title: Causal Inference in Network-Dependent Observational Data
Author(s): Oleg Sofrygin* and Mark van der Laan
Companies: University of California at Berkeley and University of California at Berkeley
Keywords: networks ; TMLE ; interference ; spillover ; semi-parametric ; simulations

We describe targeted maximum likelihood estimation in the presence of interference or spillover. Consider a dataset in which each observational unit is causally connected to other units via a known social or geographical network. For each unit we observe their baseline covariates, their exposure and their outcome, and we are interested in estimating the effect of a single time-point stochastic intervention. We propose a semi-parametric statistical model that allows for between-unit dependencies: First, unit-level exposure can depend on the baseline covariates of other connected units. Second, the unit-level outcome can depend on the baseline covariates and exposures of other connected units. We impose some restrictions on our model, e.g., assuming that the unit's exposure and outcome depend on other units as some known (but otherwise arbitrary) summary measures of fixed dimensionality. A practical application of our approach is then demonstrated with a network simulation study using two newly developed R packages: simcausal and tmlenet. We conclude by discussing an extension of our prior work towards estimation in longitudinal data (e.g., in context of contagion).

Authors who are presenting talks have a * after their name.

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