• DOI:10.18653/v1/2020.acl-main.474
Corpus ID: 218487374

Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates

@article{Keith2020TextAC,
  title={Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates},
  author={Katherine A. Keith and David Jensen and B. O'Connor},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.00649}
}

Katherine A. Keith, David Jensen, B. O'Connor

Published 2020

Computer Science

ArXiv

Many applications of computational social science aim to infer causal conclusions from non-experimental data. Such observational data often contains confounders, variables that influence both potential causes and potential effects. Unmeasured or latent confounders can bias causal estimates, and this has motivated interest in measuring potential confounders from observed text. For example, an individual's entire history of social media posts or the content of a news article could provide a rich… CONTINUE READING

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