Causal Inference in the Presence of Interference for Randomized Experiments

About This Grant

Statistical experiments can improve our understanding of complex systems, including the communities, organizations, and local economies that make up our society. In these systems, individuals may be connected by mechanisms such as social influence, economic competition, sharing of information, or the transmission of disease. The presence of such mechanisms is known as interference. Interference greatly complicates statistical analysis. It weakens the conclusions that may be drawn from an experiment, and requires the usage of assumptions whose correctness may be difficult to judge. As a result, statistical conclusions drawn under interference can have considerable caveats or limitations. This project will study interference and how it can be more safely modeled. Doing so can help researchers think more clearly about their experimental results when interference is present. It can also help researchers make fewer assumptions when they interpret their data. This research will help investigators in fields like economics, which influence daily lives of people in society. The project will extend a promising approach for detecting and describing interference, so that it may be applied to a broader variety of settings with no assumptions beyond what is known about the design of an experiment. It will also develop a new semiparametric approach for modeling interference, which can help researchers to draw credible statistical conclusions even when interference is strongly measurable over long distances. These conclusions will include test statistics with improved standard errors, and confidence intervals for numbers of individuals violating assumptions about interference. As part of this project, freely available software will be released to help researchers use the methods that are developed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.