Eli Sherman – “Identification Theory in Segregated Graph Causal Models”
Please join us for the MINDS & CIS Seminar Series
Tuesday, June 16, 2020 at 12:00 pm Eastern Time (US and Canada)
“Identification Theory in Segregated Graph Causal Models” by Eli Sherman (CS, JHU)
Seminar will be remote via Zoom
Join Zoom Meeting
https://wse.zoom.us/j/95178918871?pwd=bjh0M0VjMHczVXdZMnlxN0xyR2VqUT09
Meeting ID: 915 7891 8871
Password: clark_hall
Abstract – In recent years there has been an explosion of interest in causal inference methodologies in the machine learning and broader data science communities. A key issue at the foundation of all causal analysis is the concept of identification: the work of Pearl and others has sought to formally characterize when causal queries are estimable from available (i.e. observed) data given the assumed causal model. In this talk I’ll discuss extensions to the classical latent-variable DAG identification framework that are particularly relevant when there is dependence among data samples, such as social networking and spatial settings. Toward this end, I will introduce the segregated graph model, a super model for latent-variable DAGs, and argue for its use in these dependent settings. I will then provide sound and complete identification results for ‘node’ (i.e. classical) interventions. Finally, I will describe sound and complete results for the identification of ‘policy’ interventions, corresponding to a sequential decision-making setting, in segregated graphs and demonstrate how these results generalize and nest several existing identification results.
Bio – Eli Sherman is a PhD student in the Computer Science Department at Johns Hopkins University. He develops methods for obtaining causal inferences in social networking and dependent data contexts as well as approaches for intervention tailoring. He is interested in applications of these methods to healthcare, economics, and public policy. Eli is supervised by Ilya Shpitser and is affiliated with the Malone Center for Engineering in Healthcare and the Mathematical Institute for Data Science, from which he receives support through the MINDS PhD Fellowship.