@InProceedings{paul:2017:CoNLL,
  author    = {Paul, Michael J.},
  title     = {Feature Selection as Causal Inference: Experiments with Text Classification},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {163--172},
  abstract  = {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.},
  url       = {http://aclweb.org/anthology/K17-1018}
}