Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference Using Five Empirical Applications

@article{Keele2018ComparingCP,
  title={Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference Using Five Empirical Applications},
  author={Luke J. Keele and Dylan S. Small},
  journal={The American Statistician},
  year={2018},
  volume={75},
  pages={355 - 363}
}
Abstract When investigators seek to estimate causal effects, they often assume that selection into treatment is based only on observed covariates. Under this identification strategy, analysts must adjust for observed confounders. While basic regression models have long been the dominant method of statistical adjustment, methods based on matching or weighting have become more common. Of late, methods based on machine learning (ML) have been developed for statistical adjustment. These ML methods… 

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