A0724
Title: Instrumental variable analysis with multivariate point process treatments
Authors: Shizhe Chen - University of California, Davis (United States) [presenting]
Zhichao Jiang - Sun Yat-sen University (China)
Yu Liu - University of Science and Technology of China (China)
Abstract: Multivariate point processes are popular tools for inferring relationships among subjects from recurrent event data such as neural spike trains. Complicated by the unmeasured confounding variables, interventions to the system are often employed in order to infer causality. However, these interventions are of low precision, and they might cause the intensity of multiple processes simultaneously. An instrumental variable framework is proposed, with treatments being multivariate point processes. It is shown that the causal effects can be learned using generalized Wald estimation. A penalized estimation procedure is proposed, motivated by classic methods for density deconvolution. The proposed method is applied to neural data from behavioral experiments on mice.
