336 citations · 466 across the 9 of their papers we have counts for
9 papers
CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees
S M Rafiuddin, Atriya Sen
Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…
REFORMS: Reporting Standards for Machine Learning Based Science
Sayash Kapoor, Emily Cantrell, Kenny Peng +16
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and ge…
Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
Naoki Egami, Musashi Hinck, Brandon M. Stewart +1
In computational social science (CSS), researchers analyze documents to explain social and political phenomena. In most scenarios, CSS researchers first obtain labels for documents…
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
Amir Feder, Katherine A. Keith, Emaad Manzoor +10
A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same…
Naïve regression requires weaker assumptions than factor models to adjust for multiple cause confounding
Justin Grimmer, Dean Knox, Brandon M. Stewart
The empirical practice of using factor models to adjust for shared, unobserved confounders, , in observational settings with multiple treatments, , is wides…
Understanding How and Why University Students Use Virtual Private Networks
Agnieszka Dutkowska-Zuk, Austin Hounsel, Andre Xiong +4
We study how and why university students chose and use VPNs, and whether they are aware of the security and privacy risks that VPNs pose. To answer these questions, we conducted 32…
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Mikhail Khodak, Nikunj Saunshi, Yingyu Liang +3
Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textua…
How to Make Causal Inferences Using Texts
Naoki Egami, Christian J. Fong, Justin Grimmer +2
New text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories of interest from large collectio…
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
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 de…