Search Results for author: Brandon M. Stewart

Found 7 papers, 1 papers with code

Naïve regression requires weaker assumptions than factor models to adjust for multiple cause confounding

no code implementations24 Jul 2020 Justin Grimmer, Dean Knox, Brandon M. Stewart

We prove under these assumptions, a na\"ive semiparametric regression of $\mathbf{Y}$ on $\mathbf{A}$ is asymptotically unbiased.

Causal Inference regression

How to Make Causal Inferences Using Texts

no code implementations6 Feb 2018 Naoki Egami, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart

We argue that nearly all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation.

How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

no code implementations30 Oct 2017 Allison J. B. Chaney, Brandon M. Stewart, Barbara E. Engelhardt

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions.

Recommendation Systems

Cannot find the paper you are looking for? You can Submit a new open access paper.