Sparse concordance-assisted learning for optimal treatment decision

Shuhan Liang, Wenbin Lu, Rui Song, Lan Wang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


To find optimal decision rule, Fan et al. (2016) proposed an innovative concordance-assisted learning algorithm which is based on maximum rank correlation estimator. It makes better use of the available information through pairwise comparison. However the objective function is discontinuous and computationally hard to optimize. In this paper, we consider a convex surrogate loss function to solve this problem. In addition, our algorithm ensures sparsity of decision rule and renders easy interpretation. We derive the L2 error bound of the estimated coefficients under ultra-high dimension. Simulation results of various settings and application to STAR*D both illustrate that the proposed method can still estimate optimal treatment regime successfully when the number of covariates is large.

Original languageEnglish (US)
Pages (from-to)1-26
Number of pages26
JournalJournal of Machine Learning Research
StatePublished - Apr 1 2018

Bibliographical note

Funding Information:
We would like to acknowledge support for this project from National Institute of Mental Health for providing the STAR*D data. We would also like to thank Dr. Eric Chi for his help in developing Spingarn’s method.

Publisher Copyright:
© 2018 Shuhan Liang, Wenbin Lu, Rui Song and Lan Wang.


  • Concordance-assisted learning
  • L1 norm
  • Optimal treatment regime
  • Support vector machine
  • Variable selection


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