Open Access
August 2017 A Paradox from Randomization-Based Causal Inference
Peng Ding
Statist. Sci. 32(3): 331-345 (August 2017). DOI: 10.1214/16-STS571


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 a lot of controversies and confusions for both theoretical and practical statisticians, a careful comparison between the two approaches has been lacking in the literature for more than eighty years. We fill this historical gap by making a theoretical comparison between them and highlighting an intriguing paradox that has not been recognized by previous researchers. Logically, Fisher’s null implies Neyman’s null. It is therefore surprising that, in actual completely randomized experiments, rejection of Neyman’s null does not imply rejection of Fisher’s null for many realistic situations, including the case with constant causal effect. Furthermore, we show that this paradox also exists in other commonly-used experiments, such as stratified experiments, matched-pair experiments and factorial experiments. Asymptotic analyses, numerical examples and real data examples all support this surprising phenomenon. Besides its historical and theoretical importance, this paradox also leads to useful practical implications for modern researchers.


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Peng Ding. "A Paradox from Randomization-Based Causal Inference." Statist. Sci. 32 (3) 331 - 345, August 2017.


Published: August 2017
First available in Project Euclid: 1 September 2017

zbMATH: 06870245
MathSciNet: MR3695995
Digital Object Identifier: 10.1214/16-STS571

Keywords: Average null hypothesis , Fisher randomization test , potential outcome , randomized experiment , repeated sampling property , sharp null hypothesis

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.32 • No. 3 • August 2017
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