Causal inference in network settings

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Many statistical problems aim to answer etiological questions: does exposure Z cause changes in an outcome Y, when accounting for possible biasing factors such as non-random sampling (selection bias), measurement error, missing data, or confounding? Causal inference offers a means of formalizing the assumptions that are required to draw causal conclusions from an analysis. While causal methods are routinely used for simple settings such as point or even longitudinal exposures, there are many new frontiers to explore in this exciting branch of statistics. The proposed research will tackle causality in several new settings, where traditional regression techniques are not appropriate. I will focus on three main objectives related to causal inference: (1) to extend causal methods in the context of environmental data; (2) to develop causal estimators for use in respondent-driven samples; and (3) to estimate sequential tailored decision-rules from non-experimental data that are stored across a distributed network of sites who cannot share individual-level data. These three objectives all tackle different aspect of causal inference in a network, with different challenges arising depending on the type of network structure. In environmental data, data are often spatially distributed, and the commonly-made assumption of "no interference" is violated. In respondent-driven sampling, the network structure arises due to a sampling design in which participants recruit their contacts and hence the sample units are not independent and, for some exposures, interference may again arise. The third aim tackles a very different problem: while data within a given site may be correlated, individual measures are unavailable to the analyst, and only pooled or summary information is available to learn about tailored decision strategies. The impact of this work will be to change the manner in which researchers carry out analyses designed to achieve important research goals, ranging from understanding the impact of environmental exposures on economic outcomes to better using data from multiple sites without the risk of privacy breaches that occur when full data are shared. The work will also provide important insights into the impact of correlation and complex network structures on the types of questions that can be answered causally, and the estimators used to answer them.