English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Causal understanding is essential for many kinds of decision-making, butcausal inference from observational data has typically only been applied tostructured, low-dimensional datasets. While text classifiers producelow-dimensional outputs, their use in causal inference has not previously beenstudied. To facilitate causal analyses based on language data, we consider therole that text classifiers can play in causal inference through establishedmodeling mechanisms from the causality literature on missing data andmeasurement error. We demonstrate how to conduct causal analyses using textclassifiers on simulated and Yelp data, and discuss the opportunities andchallenges of future work that uses text data in causal inference.

Challenges of Using Text Classifiers for Causal Inference