Publications by Year: Working Paper

Working Paper
Chaney AJB, Stewart BM, Engelhardt BE. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. [Internet]. Working Paper. arXiv
Egami N, Fong CJ, Grimmer J, Roberts ME, Stewart BM. How to Make Causal Inferences Using Texts. Working Paper. ais.pdf
Roberts ME, Stewart BM, Nielsen R. Adjusting for Confounding with Text Matching. Working Paper. textmatchingfeb2018.pdf

NB: This paper is a revised version of the manuscript formerly titled "Matching Methods for High-Dimensional Data with Applications to Text"

Stewart BM. Latent Factor Regressions for the Social Sciences. Working Paper.Abstract

In this paper I present a general framework for regression in the presence of complex dependence structures between units such as in time-series cross-sectional data, relational/network data, and spatial data. These types of data are challenging for standard multilevel models because they involve multiple types of structure (e.g. temporal effects and cross-sectional effects) which are interactive. I show that interactive latent factor models provide a powerful modeling alternative that can address a wide range of data types. Although related models have previously been proposed in several different fields, inference is typically cumbersome and slow. I introduce a class of fast variational inference algorithms that allow for models to be fit quickly and accurately.

tensorreg.pdf tensorregappendix.pdf