Synthesized difference in differences

@article{Strobl2021SynthesizedDI,
  title={Synthesized difference in differences},
  author={Eric V. Strobl and Thomas A. Lasko},
  journal={Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics},
  year={2021}
}
  • Eric V. Strobl, T. Lasko
  • Published 2 May 2021
  • Computer Science
  • Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
We consider estimating the conditional average treatment effect for everyone by eliminating confounding and selection bias. Unfortunately, randomized clinical trials (RCTs) eliminate confounding but impose strict exclusion criteria that prevent sampling of the entire clinical population. Observational datasets are more inclusive but suffer from confounding. We therefore analyze RCT and observational data simultaneously in order to extract the strengths of each. Our solution builds upon… 

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