BSU Seminar-"Hierarchical Causal Models" This is a free seminar- to attend remotely please use the following link: https://teams.microsoft.com/meet/3978637967598328?p=nw8RVurjolXhkz4sKy

Speaker: David Blei, Columbia University
Series: MRC Biostatistics Unit Seminars

Hierarchical Causal Models

David Blei

Columbia University

Analyzing nested data with hierarchical models is a staple of Bayesian statistics, but causal modeling remains largely focused on "flat" models. In this talk, we will explore how to think about nested data in causal models, and we will consider the advantages of nested data over aggregate data (such as data means) for causal inference. We show that disaggregating your data---replacing a flat causal model with a hierarchical causal model---can provide new opportunities for identification and estimation. As examples, we will study how to identify and estimate causal effects under unmeasured confounders, interference, and instruments.

Paper: https://www.jmlr.org/papers/v27/25-0899.html

This is joint work with Eli Weinstein.

This is a free seminar- to attend remotely please use the following link: