Hierarchical Causal Models

arXiv:2401.05330v2 Announce Type: replace-cross
Abstract: Scientists often want to learn about cause and effect from hierarchical data, collected from subunits nested inside units. Consider students in schools, cells in patients, or cities in states. In such settings, unit-level variables (e.g. each school's budget) may affect subunit-level variables (e.g. the test scores of each student in each school) and vice versa. To address causal questions with hierarchical data, we propose hierarchical causal models, which extend structural causal models and causal graphical …

abstract arxiv budget causal cause and effect cells cities data hierarchical inside learn patients replace school schools scientists stat.me stat.ml students test type units variables