CAREER: CCF: CIF: Randomized Experimentation for Systems with Time-varying Dynamics and Network Interference

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Randomized experiments are widely used to estimate causal effects of proposed treatments in a range of domains, including the natural and social sciences, engineering, health care, and the tech industry. However, many modern systems involve complex dependencies across individuals and time, referred to as interference. The interference violates assumptions that are critical to the performance of conventional causal estimators. Without adjusting for interference, existing approaches can result in incorrect conclusions that have detrimental downstream impact on policy decisions. This project focuses on developing a new toolkit of methods for causal inference that address interference arising from network interactions and time dynamics. The project will develop a causal inference software package that will be used as a vehicle to integrate the research into education and outreach to pre-college students, undergraduate student researchers, and industry collaborators.The project will consider settings where the dependencies arise from direct impact of the treatments of neighbors on the outcome of an individual, as well as settings where the impacts are mediated through state variables such that the treatment effect may propagate across the network over time. The investigator will explore different modeling assumptions to facilitate estimation given the complexities of interference, while aiming for robust solutions and analyses that can be generalized to broad classes of models. The developed techniques will illuminate the implications of known structure and richness of available measurements on the feasibility of estimation by exploiting the interaction between model assumptions, the choice of the randomized experimental design and estimator, and achievable guarantees. The educational component of the project will also involve building a software package that enables individuals and researchers to simulate randomized experiments and observational studies across various applications to benchmark new solution concepts.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.