• Corpus ID: 88513475

Causal inference for ordinal outcomes

@article{Volfovsky2015CausalIF,
  title={Causal inference for ordinal outcomes},
  author={Alexander Volfovsky and Edoardo M. Airoldi and Donald B. Rubin},
  journal={arXiv: Methodology},
  year={2015}
}
Many outcomes of interest in the social and health sciences, as well as in modern applications in computational social science and experimentation on social media platforms, are ordinal and do not have a meaningful scale. Causal analyses that leverage this type of data, termed ordinal non-numeric, require careful treatment, as much of the classical potential outcomes literature is concerned with estimation and hypothesis testing for outcomes whose relative magnitudes are well defined. Here, we… 

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