Challenges of Using Text Classifiers for Causal Inference.

Area of Science:

• Computational Social Science
• Causal Inference
• Natural Language Processing

Background:

• Causal understanding is crucial for decision-making.
• Traditional causal inference is limited to structured, low-dimensional data.
• Text classifiers offer low-dimensional outputs but haven't been used for causal inference.

Purpose of the Study:

• To investigate the utility of text classifiers in causal inference.
• To adapt existing causal inference methods for language data.
• To bridge the gap between text analysis and causal reasoning.

Main Methods:

• Utilizing text classifiers to generate low-dimensional representations from text.
• Applying established causal inference techniques for missing data and measurement error.
• Demonstrating methods on simulated datasets and real-world Yelp data.

Main Results:

• Successfully applied text classifiers within causal inference frameworks.
• Showcased the feasibility of causal analysis using language data.
• Identified practical applications and potential limitations.

Conclusions:

• Text classifiers can be effectively integrated into causal inference.
• Language data offers a new frontier for causal discovery and analysis.
• Further research is needed to explore the full potential and challenges.