A paradox from randomization-based causal inference

@article{Ding2014APF,
  title={A paradox from randomization-based causal inference},
  author={Peng Ding},
  journal={arXiv: Statistics Theory},
  year={2014}
}
  • P. Ding
  • Published 2 February 2014
  • Mathematics
  • arXiv: Statistics Theory
Under the potential outcomes framework, causal effects are defined as comparisons between potential outcomes under treatment and control. To infer causal effects from randomized experiments, Neyman proposed to test the null hypothesis of zero average causal effect (Neyman's null), and Fisher proposed to test the null hypothesis of zero individual causal effect (Fisher's null). Although the subtle difference between Neyman's null and Fisher's null has caused lots of controversies and confusions… 

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