• Corpus ID: 173990271

An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference

@article{Shimoni2019AnET,
  title={An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference},
  author={Yishai Shimoni and Ehud Karavani and Sivan Ravid and Peter Bak and Tan Hung Marie Ng and Sharon Hensley Alford and Denise Meade and Yaara Goldschmidt},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.00442}
}
Real world observational data, together with causal inference, allow the estimation of causal effects when randomized controlled trials are not available. To be accepted into practice, such predictive models must be validated for the dataset at hand, and thus require a comprehensive evaluation toolkit, as introduced here. Since effect estimation cannot be evaluated directly, we turn to evaluating the various observable properties of causal inference, namely the observed outcome and treatment… 

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