Unveiling Causal Effects: Assistant Professor Xinran Li's NSF CAREER Grant Revolutionizes Statistical Methodologies

Professor Xinran Li of the University of Illinois Urbana-Champaign Department of Statistics has been awarded a prestigious NSF CAREER Proposal grant. The grant, titled "Advances in Randomization Inference for Causal Effects: Heterogeneity, Sensitivity, and Complexity”, is funded by the Division of Mathematical Sciences (DMS) within the National Science Foundation (NSF). The CAREER program, known as the Faculty Early Career Development Program, is a highly esteemed initiative that spans across the NSF. It aims to recognize and support early-career faculty members who demonstrate the potential to serve as academic role models in both research and education while making significant contributions to their respective fields.

Professor Xinran Li, the Principal Investigator for the project, is an accomplished researcher in the field of statistics. He holds a Ph.D. in Statistics from Harvard University, earned in 2018 under the guidance of Jun S. Liu and Donald B. Rubin. Before joining the University of Illinois, Professor Li worked as a postdoctoral researcher at the University of Pennsylvania. He completed his undergraduate studies at Peking University, receiving a B.S. in Mathematics and Applied Mathematics with a minor in Economics in 2013.

The research project aims to develop innovative statistical methodologies that enhance our understanding of causal effects. These methodologies will address various challenges related to heterogeneity, sensitivity, and complexity in causal inference. The project's outcomes have far-reaching implications in fields such as political science, education, and sociology. For example, the research can provide insights into the proportion of individuals benefiting from specific policies beyond average treatment effects.

In addition to publishing research findings in academic journals, Professor Xinran Li plans to disseminate the results through presentations and the distribution of open-source software. The project also includes educational and outreach activities, which will integrate with the research agenda. These activities aim to enhance undergraduate education, promote causality knowledge to broader audiences, and equip graduate students with essential skills for advanced research and teaching.

The project's primary objectives revolve around developing new tools for understanding causal effects in both randomized experiments and observational studies. The methodologies will build upon or be inspired by randomization inference, which utilizes the randomization of treatment assignments as a foundation for analysis. Specifically, the project will focus on three key areas. Firstly, the research will develop inference techniques to estimate the distribution of individual causal effects, which is often challenging due to unidentifiability from observed data. Secondly, the project will provide new sensitivity analyses capable of accommodating extreme hidden confounding in observational studies, strengthening the causal conclusions. Lastly, the research will develop robust inference methods for complex randomized experiments, going beyond simple randomization or considering peer influence.

To facilitate the practical application of these new tools, the project will create computationally efficient algorithms and publicly available R software packages. This will enable researchers and practitioners to utilize the methodologies in various applications.

The award to professor Xinran Li reflects the NSF's commitment to supporting research that demonstrates intellectual merit and broader impacts. Through his research and educational activities, Professor Li aims to advance the field of causal inference, contribute to decision-making processes, and promote evidence-based policy formulation across a wide range of disciplines.

Through the prestigious CAREER award, professor Xinran Li of the University of Illinois Department of Statistics has not only achieved recognition for his exceptional potential but has also received valuable support to advance his research and educational endeavors. This award will undoubtedly contribute to his development as a leading scholar and educator in the field of statistics, while also promoting the NSF's broader mission of advancing scientific knowledge and fostering inclusive excellence.


Aaron Thompson

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