This paper proposes a matching technique for learning causal associations between word features and class labels in document classification. The goal is to identify more meaningful and generalizable features than with only correlational approaches. Experiments with sentiment classification show that the proposed method identifies interpretable word associations with sentiment and improves classification performance in a majority of cases. The proposed feature selection method is particularly effective when applied to out-of-domain data.
REF tackled the problem of learning causal associations between word features and class labels using propensity matching for the task of sentiment analysis.
Feature Selection as Causal Inference: Experiments with Text Classification
CoNLL
Computer Science