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Found 11 papers, 6 papers with code
Proximal Causal Inference With Text Data
Recent text-based causal methods attempt to mitigate confounding bias by including unstructured text data as proxies of confounding variables that are partially or imperfectly measured.
RCT Rejection Sampling for Causal Estimation Evaluation
We contribute a new sampling algorithm, which we call RCT rejection sampling, and provide theoretical guarantees that causal identification holds in the observational data to allow for valid comparisons to the ground-truth RCT.
Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications
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.
Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects
Using observed language to understand interpersonal interactions is important in high-stakes decision making.
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
A fundamental goal of scientific research is to learn about causal relationships.
Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence
Automated event extraction in social science applications often requires corpus-level evaluations: for example, aggregating text predictions across metadata and unbiased estimates of recall.
Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty
We find for this application (1) some annotator disagreements of economic policy uncertainty can be attributed to ambiguity in language, and (2) switching measurements from keyword-matching to supervised machine learning classifiers results in low correlation, a concerning implication for the validity of the index.
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates
For example, an individual's entire history of social media posts or the content of a news article could provide a rich measurement of multiple confounders.
Modeling financial analysts' decision making via the pragmatics and semantics of earnings calls
Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts.
Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses
Dependency parsing research, which has made significant gains in recent years, typically focuses on improving the accuracy of single-tree predictions.
Identifying civilians killed by police with distantly supervised entity-event extraction
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.
