Causal Inference with Massive and Complex data: High-dimensionality and Network Interference

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Modern observational databases hold great promise for new discoveries. To date, a lot of advances have been made by finding associations in massive and complex data sets. Finding patterns of associations is an important first step, but does not by itself lead to actionable insights. This motivates the proposed program to go beyond association and assess causation in "big data" settings, so that we can better understand the infinite complexity of nature, and use such knowledge to make better decisions. Specifically we focus on addressing two key challenges posed by contemporary massive and complex data sets: high-dimensionality and complex network structure. We are going to develop a novel set of methods driven by applications in data-intensive fields such as neuroimaging, genetics and network analysis. These advances will push the frontiers of causal inference beyond conventional settings, and at the same time bring causal thinking to big data analytics. This program will also provide training opportunities for promising undergraduate and graduate students as well as postdoctoral researchers, especially those from historically underrepresented groups. Methods developed through this grant will be coded in R and made publicly available along with publication of related research reports.