Randomization inference for the trimmed mean of effects attributable to treatment

Xingdong Feng, Yang Feng, Yuguo Chen, Dylan S. Small

Research output: Contribution to journalArticlepeer-review


Randomization is described by Fisher (1935) as the reasoned basis for inference about the effectiveness of treatments. Fisher advocated both using randomization in designing experiments and using "randomization inference" to analyze experiments that have been randomized. Randomization inference is inference that assumes only the physical act of randomization for its validity. It provides exact, distribution free inferences in randomized experiments. In this paper, we expand the scope of randomization inference by developing randomization inference for the trimmed mean of effects attributable to treatment. Trimmed means of the effects attributable to treatment are interpretable summaries of the treatment effect that are robust to outliers. We connect the inference problem for trimmed means of effects attributable to treatment to a multiple choice knapsack problem, and use an efficient combinatorial optimization algorithm.

Original languageEnglish (US)
Pages (from-to)773-797
Number of pages25
JournalStatistica Sinica
Issue number2
StatePublished - Apr 1 2014


  • Knapsack problem
  • Observational study
  • Randomization inference
  • Trimmed mean

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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