Fast causal inference with non-random missingness by test-wise deletion

@article{Strobl2017FastCI,
  title={Fast causal inference with non-random missingness by test-wise deletion},
  author={Eric V. Strobl and Shyam Visweswaran and Peter L. Spirtes},
  journal={International Journal of Data Science and Analytics},
  year={2017},
  volume={6},
  pages={47-62}
}
Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before applying causal discovery algorithms. List-wise deletion is a sound and general strategy when paired with algorithms such as FCI and RFCI, but the deletion procedure also eliminates otherwise good samples that contain only a few missing values. In this report, we show that we can more efficiently utilize the observed… CONTINUE READING