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

Abstract

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.

Citation

Download Citation

Peng Ding. "A Paradox from Randomization-Based Causal Inference." Statist. Sci. 32 (3) 331 - 345, August 2017. https://doi.org/10.1214/16-STS571

Information

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

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

Rights: Copyright © 2017 Institute of Mathematical Statistics

JOURNAL ARTICLE
15 PAGES


SHARE
Vol.32 • No. 3 • August 2017
Back to Top