Randomization Inference for Treatment Effect Variation

  title={Randomization Inference for Treatment Effect Variation},
  author={Peng Ding and Avi Feller and Luke Miratrix},
Applied researchers are increasingly interested in whether and how treatment effects vary in randomized evaluations, especially variation that is not explained by observed covariates. We propose a model-free approach for testing for the presence of such unexplained variation. To use this randomization-based approach, we must address the fact that the average treatment effect, which is generally the object of interest in randomized experiments, actually acts as a nuisance parameter in this… CONTINUE READING
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