Work

A randomization-based perspective on analysis of variance: a test statistic robust to treatment effect heterogeneity

Year: 2017

Type: article

Abstract: Fisher randomization tests for Neyman’s null hypothesis of no average treatment effect are considered in a finite-population setting associated with completely randomized experiments involving more than two treatments. The consequences of using the |$F$| statistic to conduct such a test are examined, and we argue that under treatment effect heterogeneity, use of the |$F$| statistic in the Fisher randomization test can severely inflate the Type I error under Neyman’s null hypothesis. We propose to use an alternative test statistic, derive its asymptotic distributions under Fisher’s and Neyman’s null hypotheses, and demonstrate its advantages through simulations.

Source: Biometrika

DOI: 10.1093/biomet/asx059

Authors: Peng Ding, Tirthankar Dasgupta

Institutions: University of California, Berkeley, Rutgers, The State University of New Jersey

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: Institute of Education Sciences

OpenAlex ID: W2964258855

FWCI: 1.048

Cites: 44

Cited by: 16

Related to: 10