In this talk, we examine causal quantities of interest and establish nonparametric identification results with a binary instrumental variable. We also propose an estimation strategy based on well-established deconvolution methods, which is applicable under many commonly-used models without requiring distributional assumptions on the unmeasured confounders. For empirical illustration, we apply the proposed methodology to the analysis of data from an optogenetic experiment that studies how neural circuits maintain stable odor representation in the mouse brain.

Bio: Shizhe Chen is an assistant professor in the Department of Statistics at University of California, Davis. He is an alumnus of Yuanpei College, and he obtained his Ph.D. in biostatistics at University of Washington. He was a postdoctoral research scientist at Department of Statistics and Grossman Center for the Statistics of Mind in Columbia University. His recent research focuses on development statistical theory and methods for neural data arising from modern neuroscience experiments.