15 citations · 45 across the 13 of their papers we have counts for
13 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…
Don't Forget It! Conditional Sparse Autoencoder Clamping Works for Unlearning
Matthew Khoriaty, Andrii Shportko, Gustavo Mercier +1
Recent developments in Large Language Model (LLM) capabilities have brought great potential but also posed new risks. For example, LLMs with knowledge of bioweapons, advanced chemi…
Can You Trust LLM Judgments? Reliability of LLM-as-a-Judge
Kayla Schroeder, Zach Wood-Doughty
Large Language Models (LLMs) have become increasingly powerful and ubiquitous, but their stochastic nature poses challenges to the reliability of their outputs. While deterministic…
Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status
Samuel Lee, Zach Wood-Doughty
Causal understanding is a fundamental goal of evidence-based medicine. When randomization is impossible, causal inference methods allow the estimation of treatment effects from ret…
Reliability of Topic Modeling
Kayla Schroeder, Zach Wood-Doughty
Topic models allow researchers to extract latent factors from text data and use those variables in downstream statistical analyses. However, these methodologies can vary significan…
Segment Anything Model is a Good Teacher for Local Feature Learning
Jingqian Wu, Rongtao Xu, Zach Wood-Doughty +3
Local feature detection and description play an important role in many computer vision tasks, which are designed to detect and describe keypoints in "any scene" and "any downstream…
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…
The Proximal ID Algorithm
Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen
Unobserved confounding is a fundamental obstacle to establishing valid causal conclusions from observational data. Two complementary types of approaches have been developed to addr…
Faithful and Plausible Explanations of Medical Code Predictions
Zach Wood-Doughty, Isabel Cachola, Mark Dredze
Machine learning models that offer excellent predictive performance often lack the interpretability necessary to support integrated human machine decision-making. In clinical medic…
Generating Synthetic Text Data to Evaluate Causal Inference Methods
Zach Wood-Doughty, Ilya Shpitser, Mark Dredze
Drawing causal conclusions from observational data requires making assumptions about the true data-generating process. Causal inference research typically considers low-dimensional…
Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement
Aaron Mueller, Zach Wood-Doughty, Silvio Amir +2
The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence. While #MeToo has been praised for providing support for self-disclosur…
Using Noisy Self-Reports to Predict Twitter User Demographics
Zach Wood-Doughty, Paiheng Xu, Xiao Liu +1
Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twit…
Challenges of Using Text Classifiers for Causal Inference
Zach Wood-Doughty, Ilya Shpitser, Mark Dredze
Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional dat…