Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020.acl-main)
Copied to clipboard
| Challenge: | Unmeasured or latent confounders can bias causal estimates and this has motivated interest in measuring potential confounder from observed text. |
| Approach: | They propose to use text to measure potential confounders in a way that allows for a rich measurement of multiple confounder variables. |
| Outcome: | The proposed method is based on an individual’s entire history of social media posts or the content of a news article. |
Similar Papers
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (2022.tacl-1)
Copied to clipboard
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. |
Causal Inference from Text: Unveiling Interactions between Variables (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for estimating causal effects from text only account for latent covariates that affect both treatment and outcome. |
| Approach: | They propose to disentangle non-confounding covariates from text to minimize selection bias . they conduct experiments on two different treatment factors under various scenarios . |
| Outcome: | The proposed model outperforms strong baselines on earnings call transcripts . the proposed model is based on a randomized controlled trial . |
Challenges of Using Text Classifiers for Causal Inference (D18-1)
Copied to clipboard
| 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. |
A Review of Dataset and Labeling Methods for Causality Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for causal relationship extraction are limited and lack of unified methods hinder progress in the field. |
| Approach: | They propose to summarize existing methods and propose a new causal sequence label method . they propose to use multiple candidate causal label sequences according to label controversy . |
| Outcome: | The proposed method summarises existing methods and explores their practicability and extensibility from multiple perspectives. |
Conceptualizing Treatment Leakage in Text-based Causal Inference (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to control for text-based confounders rely on assumption that there is no treatment leakage . prior literature has assumed that documents only contain information about confounder, but not about treatment assignment. |
| Approach: | They define the treatment leakage problem and propose methods to mitigate it . they remove treatment-related signal from text in a pre-processing step . |
| Outcome: | The proposed method can mitigate the problem of treatment leakage by removing the treatment-related signal from the text. |
DoubleLingo: Causal Estimation with Large Language Models (2024.naacl-short)
Copied to clipboard
| 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. |
CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing (2022.emnlp-tutorials)
Copied to clipboard
| Challenge: | Establishing causal relationships is a fundamental goal of scientific research . lack of clear definitions, notations, benchmark datasets, and challenges remains . |
| Approach: | They introduce the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provide an overview of causal perspectives to NLP problems. |
| Outcome: | This tutorial introduces the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provides an overview of causal perspectives to NLP problems. |
Causal Inference of Script Knowledge (2020.emnlp-main)
Copied to clipboard
| Challenge: | Prior work on script induction relied on correlation between instances of events in corpus . instead, we propose an approach based on causal effects between events . |
| Approach: | They propose to use causal effects to induce scripts from text . they propose to compute a function that matches the intuition of what a script represents . |
| Outcome: | The proposed method matches the intuition of what a script represents, the authors show . |
Causal Inference with Large Language Model: A Survey (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing causal inference frameworks do not match human judgment in several key areas, such as domain knowledge, logical inference, and cultural context. |
| Approach: | They propose to apply large language models to causal inference tasks . they summarize the main causal problems and approaches and compare their results . |
| Outcome: | The proposed methods are compared with traditional methods in healthcare, finance, and economics. |
Causal Matching with Text Embeddings: A Case Study in Estimating the Causal Effects of Peer Review Policies (2023.findings-acl)
Copied to clipboard
| Challenge: | Using a method that uses text embeddings, we find that human judges prefer manuscript matches from our method in 70% of cases. |
| Approach: | They propose to use text embeddings to integrate a method to estimate the causal effects of peer review policies in publication venues that shift policies from single-blind to double-blinded. |
| Outcome: | The proposed method is compared with a widely-used method of stratified propensity score matching and a baseline of randomly selected matches. |