15 citations · 19 across the 12 of their papers we have counts for
14 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…
Exploring Sparse Autoencoders in Text-Based Causal Confounding Adjustment
Mian Zhong, Katherine A. Keith, Anjalie Field
In many settings, studying causal questions based on text data requires adjusting for confounding information within texts. Yet there is a tradeoff in constructing text representat…
Op-Fed: Opinion, Stance, and Monetary Policy Annotations on FOMC Transcripts Using Active Learning
Alisa Kanganis, Katherine A. Keith
The U.S. Federal Open Market Committee (FOMC) regularly discusses and sets monetary policy, affecting the borrowing and spending decisions of millions of people. In this work, we r…
Proximal Causal Inference With Text Data
Jacob M. Chen, Rohit Bhattacharya, Katherine A. Keith
Recent text-based causal methods attempt to mitigate confounding bias by estimating proxies of confounding variables that are partially or imperfectly measured from unstructured te…
RCT Rejection Sampling for Causal Estimation Evaluation
Katherine A. Keith, Sergey Feldman, David Jurgens +2
Confounding is a significant obstacle to unbiased estimation of causal effects from observational data. For settings with high-dimensional covariates -- such as text data, genomics…
Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications
Li Lucy, Jesse Dodge, David Bamman +1
Scholarly text is often laden with jargon, or specialized language that can facilitate efficient in-group communication within fields but hinder understanding for out-groups. In th…
Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects
Katherine A. Keith, Douglas Rice, Brendan O'Connor
Using observed language to understand interpersonal interactions is important in high-stakes decision making. We propose a causal research design for observational (non-experimenta…
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…
Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence
Andrew Halterman, Katherine A. Keith, Sheikh Muhammad Sarwar +1
Automated event extraction in social science applications often requires corpus-level evaluations: for example, aggregating text predictions across metadata and unbiased estimates…
Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty
Katherine A. Keith, Christoph Teichmann, Brendan O'Connor +1
Methods and applications are inextricably linked in science, and in particular in the domain of text-as-data. In this paper, we examine one such text-as-data application, an establ…
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates
Katherine A. Keith, David Jensen, Brendan O'Connor
Many applications of computational social science aim to infer causal conclusions from non-experimental data. Such observational data often contains confounders, variables that inf…
Modeling financial analysts' decision making via the pragmatics and semantics of earnings calls
Katherine A. Keith, Amanda Stent
Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target reco…
Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses
Katherine A. Keith, Su Lin Blodgett, Brendan O'Connor
Dependency parsing research, which has made significant gains in recent years, typically focuses on improving the accuracy of single-tree predictions. However, ambiguity is inheren…
Identifying civilians killed by police with distantly supervised entity-event extraction
Katherine A. Keith, Abram Handler, Michael Pinkham +3
We propose a new, socially-impactful task for natural language processing: from a news corpus, extract names of persons who have been killed by police. We present a newly collected…