Papers by Zach Wood-Doughty
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (2022.tacl-1)
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Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
| Challenge: | causality has not had the same importance in natural language processing, says aaron e. smith . he says research on causality in NLP remains scattered across domains without unified definitions . |
| Approach: | They propose to consolidate research on causality in NLP across academic areas . they explore potential uses of causal inference to improve robustness, fairness, interpretability . |
| Outcome: | The proposed method is a unified overview of causal inference for the NLP community. |
Reliability of Topic Modeling (2025.naacl-long)
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| Challenge: | Topic models allow researchers to extract latent factors from text data and use those variables in downstream statistical analyses. |
| Approach: | They propose to use McDonald's as a benchmark to evaluate topic model reliability. |
| Outcome: | The proposed model is based on McDonald's , which provides the best encapsulation of reliability on synthetic and real-world data. |
Challenges of Using Text Classifiers for Causal Inference (D18-1)
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| Challenge: | a number of scientific analyses focus on low-dimensional structured data, but text classifiers can be used to produce structured variables. |
| Approach: | They propose to use text classifiers to conduct causal analyses on simulated and Yelp data. |
| Outcome: | The proposed method can be used on simulated and Yelp data. |
DoubleLingo: Causal Estimation with Large Language Models (2024.naacl-short)
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| Challenge: | Existing methods for causal estimation are inadequate for noisy text data. |
| Approach: | They propose to use LLM-based nuisance models to estimate causal effects from non-randomized data using assumptions about the underlying data distribution. |
| Outcome: | The proposed method reduces the relative absolute error by 10.4% over existing methods on the best available dataset. |