Causal Feature Selection with Dimension Reduction for Interpretable Text Classification

TL;DR

This paper introduces a novel causal feature selection framework combining dimension reduction with causal inference to improve interpretability and classification accuracy in high-dimensional text data.

Contribution

It proposes a new method that integrates dimension reduction with causal inference, addressing limitations of existing causal feature selection in high-dimensional text data.

Findings

Enhanced classification performance on real-world datasets

Improved interpretability of selected features

Effective in high-dimensional text feature spaces

Abstract

Text features that are correlated with class labels, but do not directly cause them, are sometimesuseful for prediction, but they may not be insightful. As an alternative to traditional correlation-basedfeature selection, causal inference could reveal more principled, meaningful relationships betweentext features and labels. To help researchers gain insight into text data, e.g. for social scienceapplications, in this paper we investigate a class of matching-based causal inference methods fortext feature selection. Features used in document classification are often high dimensional, howeverexisting causal feature selection methods use Propensity Score Matching (PSM) which is known to beless effective in high-dimensional spaces. We propose a new causal feature selection framework thatcombines dimension reduction with causal inference to improve text feature selection. Experiments onboth synthetic and real-world data demonstrate the promise of our methods in improving classificationand enhancing interpretability.