Awesome

This is the code for the experiments in the EMNLP '18 paper, Challenges of Using Text Classifiers for Causal Inference.

To get the raw data, either:

• download latest Yelp dataset from https://www.yelp.com/dataset/download, or:
• email zach@cs.jhu.edu to get the exact data used for the paper.

To get started with the code, create a python3 virtualenv and run pip install -r requirements.txt.

To preprocess the data, run python build_yelp_dataset.py n_vocab min_freq --workdir raw_data_directory/

• n_vocab is the number of reviews to load in creating the word vocabulary.
• min_freq is the minimum frequency a word can occur in those n_vocab reviews to be included.
• the flag --n_total controls how many actual reviews to preprocess, and by default equals n_vocab.
• n_vocab and min_freq are the two params that specify a dataset in all Yelp experiments.

To run a Yelp missing data experiment, run:

python missing_data.py logn_examples --dataset yelp --n_vocab n_vocab --min_freq min_freq --workdir raw_data_directory/

To run a synthetic missing data experiment, run:

python missing_data.py logn_examples --dataset synthetic --vocab_size vocab_size

To run a Yelp measurement error experiment, run:

python measurement_error.py logn_examples --dataset yelp --n_vocab n_vocab --min_freq min_freq --workdir raw_data_directory/

To run a synthetic measurement error experiment, run:

python measurement_error.py logn_examples --dataset synthetic --vocab_size vocab_size

If you use this code, please cite:

@inproceedings{wooddoughty2018challenges,
  title={Challenges of Using Text Classifiers for Causal Inference},
  author={Wood-Doughty, Zach, Ilya Shpitser, and Mark Dredze},
  booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year={2018}
}