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1 results

  • 3 - Personalizing Medicine

  • from Part I - Personalized Medicine
  • Edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, California, Eva Enns, University of Minnesota
  • Book:
    Artificial Intelligence for Healthcare
    Published online:
    21 April 2022
    Print publication:
    05 May 2022, pp 37-59

  • Summary

    We describe fundamental challenges to estimating heterogeneous treatment effects in the context of the statistical causal inference literature, proposed algorithms for addressing those challenges, and methods to evaluate how well heterogeneous treatment effects have been estimated. We illustrate the proposed algorithms using data from two large randomized trials of blood pressure treatments. We describe directions for future research in medical statistics and machine learning in this domain. The focus will be on how flexible machine learning methods can improve causal estimators, especially in the RCT setting.