Challenges of Using Text Classifiers for Causal Inference
This work addresses the challenge of extending causal inference from structured data to text for decision-making applications, though it is incremental as it adapts existing methods to a new domain.
The paper tackled the problem of applying causal inference to text data by exploring the use of text classifiers through established causal modeling mechanisms, demonstrating this approach on simulated and Yelp datasets to facilitate causal analyses based on language data.
Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.