Academic Report of School of Mathematical Sciences [2026] No. 072

(Series Report for High-Level University Construction No. 1331)

Title：Causal inference for multivariate point process treatments via instrumental variables

Speaker：Sizhe Chen, Associate Professor (University of California, Davis)

Time：11:00-12:00, July 1, 2026

Location：Conference Room 514, Huixing Building, Yuehai Campus, Shenzhen University

Abstract: Estimating causal effects from multivariate point process data is a fundamental challenge in many scientific fields. A prominent example arises in neuroscience, where spike trains are recorded simultaneously across multiple brain areas to study how activity in some areas causally influences spiking dynamics in others. In these studies, rigorous causal inference is complicated by unobserved confounding arising from unrecorded neural activities. We propose an instrumental variable method for settings with multivariate point process treatments. We establish conditions under which the causal effects are nonparametrically identified as the unique solution to a system of multivariate convolution equations. Based on this characterization, we develop a penalized estimator and establish its consistency and asymptotic normality.  We further propose a robust hypothesis testing procedure based on a Karhunen-Loeve expansion. We apply the proposed method to Neuropixels recordings from a visual discrimination task in mice, demonstrating how activity in the visual cortex and thalamic nuclei causally influences firing patterns in the hippocampal formation.Speaker Profile：Dr. Shizhe Chen is currently an associate professor in the Department of Statistics at the University of California, Davis. Previously, he conducted postdoctoral research in the Department of Statistics at Columbia University and at the Grossman Center for the Statistics of Mind; he earned his Ph.D. in biostatistics from the University of Washington. His research interests focus primarily on high-dimensional statistics, graphical models, point processes, network inference, causal inference, and their applications in neuroscience. His work explores how to learn the structure and dynamics of biological and neural systems from large-scale, complex data, and to develop statistical methods that are both theoretically sound and practically interpretable.

Faculty and students are welcome to attend!

Invited by: Yichi Zhang

School of Mathematical Sciences

June 29, 2026